Caso 2: Impacto de la Crisis Sanitaria por COVID-19 sobre los saldos del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero
Sergio Cubero
1/6/2022
# Core
library(tidyverse)
library(grid)
library(gridExtra)
# Load data
library(readxl)
# Time Series
library(timetk)
library(forecast)
library(TSA)
library(NTS)
library(MSwM)
library(tsDyn)
library(fNonlinear)
library(dlm)
library(astsa)
library(seasonal)
library(tseries)
library(astsa)
library(tsoutliers)
library(urca)
# Machine Learning
library(tidymodels)
library(modeltime)
library(modeltime.ensemble)
library(modeltime.resample)
library(timetk)getPerformance = function(pred, val) {
res = pred - val
MAE = sum(abs(res))/length(val)
RSS = sum(res^2)
MSE = RSS/length(val)
RMSE = sqrt(MSE)
perf = data.frame(MAE, RSS, MSE, RMSE)
return(perf)
}
#getPerformance(pred, val)1.Introducción
2.Metodología
2.1. Datos
2.2. Modelo Lineal
2.3. Modelo No Lineal
2.4. Modelo Machine Learning
2.4.1. Validación Cruzada
2.5. Métricas de Rendimiento
3. Resultados
El presente apartado esta dividido en dos secciones las cuales muestran los resultados obtenidos que buscan respaldar el objetivo planteado. La primera sección se compara y selecciona el mejor modelo de pronostico de serie de tiempo según el tipo de modelo: lineales, no lineales y de minería de datos, para posteriormente, realizar un ensamble con los mejores tres métodos. La segunda sección presenta una prueba de tensión en el cual se plantearán diferentes escenarios para estimar el potencial impacto de una caída abrupta de los saldos del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero en colones y dólares en Costa Rica para diciembre 2021.
3.1. Análisis Exploratorio
En la figura @ref(fig:evolucionserie) se muestran los saldos del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero en colones y dolares para febrero-2001 a julio-2021 y de la cual se genera el análisis para identificar las siguientes características: tendencias o ciclos, existencia de estabilidad en las observaciones, variancia de las observaciones (constante o variable en el tiempo), existencia de observaciones inusuales y de puntos extremos, cambios en la estructura de la serie, entre otras.
SeriesDatos <- read_excel("~/Google Drive/Mi unidad/1.Maestria II Ciclo 2021/Curso de Analisis De Casos/Caso II/Datos/Base Datos.xlsx")%>%
janitor::clean_names()%>%
mutate(ActivoNeto=paste0(activo_neto,"-01"))%>%
rename('ActNetCRC'=crc,
'ActNetUSD'=usd)
actnetcrc<- ts(SeriesDatos[,2],start =c(2001,2),end=c(2021,7), frequency = 12)
actnetusd<- ts(SeriesDatos[,3],start =c(2001,2),end=c(2021,7), frequency = 12)
actnet <- cbind(actnetcrc,actnetusd)
fitcrc<-actnetcrc %>%
seas()
fitusd<- actnetusd %>%
seas() pseries<-autoplot(actnet,facets=TRUE) +
xlab("Mes") +
ylab("Millones")+
theme_bw()
ptendseriecr<-autoplot(actnetcrc, series="Data") +
autolayer(trendcycle(fitcrc), series="Tendencia") +
#autolayer(seasadj(fitcrc), series="Ajustada Estacionalmente") +
xlab("Mes") + ylab("Millones") +
scale_colour_manual(values=c("grey70","red","royalblue4"),
breaks=c("Data","Ajustada Estacionalmente","Tendencia"))+
theme_bw()+
ggtitle("Colones")+
geom_vline(xintercept = 2015 + (06 - 1) / 12,linetype = "dashed", colour ='gray' )+
geom_vline(xintercept = 2016 + (11 - 1) / 12,linetype = "dashed", colour ='gray' )+
scale_y_continuous(breaks = seq(0,1200000,200000))
ptendserieusd<-autoplot(actnetusd, series="Data") +
autolayer(trendcycle(fitusd), series="Tendencia") +
#autolayer(seasadj(fitusd), series="Ajustada Estacionalmente") +
xlab("Mes") + ylab("Saldos") +
ggtitle("Dolares") +
scale_colour_manual(values=c("grey70","red","royalblue4"),
breaks=c("Data","Ajustada Estacionalmente","Tendencia"))+
theme_bw()+
geom_vline(xintercept = 2015 + (06 - 1) / 12,linetype = "dashed", colour ='gray' )+
geom_vline(xintercept = 2016 + (12 - 1) / 12,linetype = "dashed", colour ='gray' )+
scale_y_continuous(breaks = seq(0,2000,250))
grid.arrange(ptendseriecr, ptendserieusd, ncol = 1)
Costa Rica:Evolución de los saldos del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero en colones y dolares, febrero-2001 a julio-2021
#https://www.sepg.pap.hacienda.gob.es/sitios/sepg/es-ES/Presupuestos/DocumentacionEstadisticas/Documentacion/Documents/DOCUMENTOS%20DE%20TRABAJO/D95006.pdf
otlier_crc<- tso(y = actnetcrc,types=c("SLS","AO","LS","TC","IO"))
plot(otlier_crc)
# otlier_usd<- tso(y = actnetusd,types=c("SLS","AO","LS","TC","IO"))
# otlier_usdA partir del análisis de la serie se identificaron las siguientes característica:
Para ambas series del activo neto , colones y dolares, se observa una tendencia creciente desde febrero 2001, así como un aumento de la variabilidad conforme aumenta los meses.
Para el periodo de mayo 2015 a octubre 2016 (lineas punteadas gris) hay un cambio de nivel (Valor extremo LS[^4]) en el volumen mensual del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero tanto en colones como en dolares, sin embargo, es inverso en ambas series, lo cual sugiere que posiblemente los participantes trasladaron sus inversiones de dolares a colones, esto se explica por:
La adopción del régimen de tipo de cambio de flotación administrada por parte del Banco Central de Costa Rica (BCCR) y el incremento en el superávit de divisas del sector privado incidió en la apreciación del colón (disminución del tipo de cambio) [@bccr5].
La reducción de la tasa de interés de política monetaria por parte del BCCR en 300 puntos base en el 2015, con el objetivo de estimular la economía, promoviendo el crecimiento en el crédito nacional y para reducir el costo de la deuda para el gobierno [@mv1; @mv2].
En el último trimestre del 2015, la industria tuvo una contracción de la liquidez en dolares, explicado por la salida de los participantes hacia el mercado internacional [@mv2].
Para el activo neto en colones se observa que en abril 2020 el activo neto en colones creció en 19.5 por ciento respecto al mismo periodo del año pasado, este comportamiento creciente y acelerado se mantuvo hasta diciembre de ese mismo año. Lo cual coincide con el efecto de la crisis sanitaria por COVID-19 que inicio en Costa Rica en marzo 2020, esta fecha es identificada como un valor extremo de cambio temporal [^4]. Esta situación sanitaria provocó un aumento de la incertidumbre en la economía mundial incidiendo en que los agentes económicos buscaran refugiarse en activos líquidos [@bccr1]. Un comportamiento similar ocurre para el activo neto en dolares.
Respecto a la estacionalidad de las series, se observa en la figura @ref(fig:estacionalidad) que para el caso de colones los saldos del activo neto tienden a ser mayores en enero y octubre, y presentar valores relativamente bajos al finalizar el año noviembre y diciembre, esto es de esperar debido a la época navideña y que diciembre comúnmente se labora 3 de las 4 semana del mes. Para el caso de dolares se observa que los meses con mayores saldos del activo neto se dan de mayo a agosto, y al igual que el caso de colones, se observa que los dos últimos meses del año los mismos se reduce.
pestacioncr <- fitcrc %>%
seasonal() %>%
ggsubseriesplot() +
ylab("Estacionalidad")+
theme_bw()+
ggtitle("Colones")
pestacionusd <- fitusd %>%
seasonal() %>%
ggsubseriesplot() +
ylab("Estacionalidad")+
theme_bw()+
ggtitle("Dolares")
grid.arrange(pestacioncr, pestacionusd, nrow = 2,ncol=1)
Costa Rica:Indice Estacional de los saldos del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero en colones y dolares, febrero-2001 a julio-2021
Por otro lado, respecto al componente irregular para la serie en colones (ver en el @ref(anexos), la figura @ref(fig:descomposicionplotanexo)) ,se comporta de hasta el año 2012 de forma aditiva, en otras, palabras la variancia de la serie no fluctua con el nivel de la serie, sin embargo, a partir de 2012 hacia delante se observa que la variación aumenta con el nivel de la serie, por lo cual se podría argumentar que la serie tiene un comportamiento mixto (aditivo y multiplicativo). En contra parte, para la serie en dolares no se observa una variación similar en todo el periodo y que no varía con respecto al nivel de la serie.
fitcrc_add<-actnetcrc %>%
decompose(type = "additive")
fitcrc_multi<-actnetcrc %>%
decompose(type = "multiplicative")
fitusd_add<- actnetusd %>%
decompose(type = "additive")
fitusd_multi<- actnetusd %>%
decompose(type = "multiplicative")
pdescompcrcadd <- fitcrc_add%>%
autoplot() +
xlab("Mes")+
ggtitle("Aditiva: Colones") +
theme_bw()
pdescompcrcmult<-fitcrc_multi%>%
autoplot() + xlab("Mes") +
ggtitle("Multiplicativa: Colones")+
theme_bw()
pdescompusdadd <- fitusd_add%>%
autoplot() +
xlab("Mes")+
ggtitle("Aditiva: Dolares") +
theme_bw()
pdescompusdmult<-fitusd_multi%>%
autoplot() + xlab("Mes") +
ggtitle("Multiplicativa: Dolares")+
theme_bw()
descompo<-grid.arrange(pdescompcrcadd,pdescompcrcmult, pdescompusdadd,pdescompusdmult, nrow = 2,ncol=2)
Para confirmar cual modelo (aditivo o multiplicativo) se ajusta mejor a cada serie se procedió a evaluar si el componente irregular identificando se ajusta a una distribución normal, para lo cual se realizaron la pruebas de hipótesis de normalidad Shapiro-Wilk y Jarque-Bera, así como una inspección gráfica por medio de Cuantil- Cuantil (qqplot). En la figura @ref(fig:irregularcrc) se puede identificar que para el caso de la serie en colones, el mejor modelo es el multiplicativo mientras que para la serie en dolares es el aditivo.
Aleatorio_Desc<-cbind(
Aleatorio_crc_add=fitcrc_add$random,
Aleatorio_crc_multi=fitcrc_multi$random,
Aleatorio_usd_add=fitusd_add$random,
Aleatorio_usd_multi=fitusd_multi$random)%>%
as.data.frame()
jb_res_crc_add<-jarque.bera.test(Aleatorio_Desc$Aleatorio_crc_add[!is.na(Aleatorio_Desc$Aleatorio_crc_add)]) # Cumple
jb_res_crc_mult<-jarque.bera.test(Aleatorio_Desc$Aleatorio_crc_multi[!is.na(Aleatorio_Desc$Aleatorio_crc_multi)]) # Cumple
jb_res_usd_add<-jarque.bera.test(Aleatorio_Desc$Aleatorio_usd_add[!is.na(Aleatorio_Desc$Aleatorio_usd_add)]) # Cumple
jb_res_usd_multi<-jarque.bera.test(Aleatorio_Desc$Aleatorio_usd_multi[!is.na(Aleatorio_Desc$Aleatorio_usd_multi)]) # Cumple
sw_res_crc_add<-shapiro.test(Aleatorio_Desc$Aleatorio_crc_add[!is.na(Aleatorio_Desc$Aleatorio_crc_add)]) # Cumple
sw_res_crc_mult<-shapiro.test(Aleatorio_Desc$Aleatorio_crc_multi[!is.na(Aleatorio_Desc$Aleatorio_crc_multi)]) # Cumple
sw_res_usd_add<-shapiro.test(Aleatorio_Desc$Aleatorio_usd_add[!is.na(Aleatorio_Desc$Aleatorio_usd_add)]) # Cumple
sw_res_usd_multi<-shapiro.test(Aleatorio_Desc$Aleatorio_usd_multi[!is.na(Aleatorio_Desc$Aleatorio_usd_multi)]) # Cumple
## Gráficosde qqplot
p1<-ggplot(Aleatorio_Desc, aes(sample = Aleatorio_crc_add))+
stat_qq() +
stat_qq_line()+
ggtitle("Aditiva - Colones") +
labs(subtitle = paste("Prubas de Normalidad (Estadístico,P-Value):Shapiro-Wilk:",round(sw_res_crc_add$statistic,3),",",round(sw_res_crc_add$p.value,4), "y",
"Jarque-Bera",round(jb_res_crc_add$statistic,3),",",round(jb_res_crc_add$p.value,4)))+
theme_bw()
p2<-ggplot(Aleatorio_Desc, aes(sample = Aleatorio_crc_multi))+
stat_qq() +
stat_qq_line()+
ggtitle("Multiplicativa - Colones")+
labs(subtitle = paste("Prubas de Normalidad (Estadístico,P-Value):Shapiro Wilk:",round(sw_res_crc_mult$statistic,3),",",round(sw_res_crc_mult$p.value,4), "y",
"Jarque-Bera",round(jb_res_crc_mult$statistic,3),",",round(jb_res_crc_mult$p.value,4)))+
theme_bw()
p3<-ggplot(Aleatorio_Desc, aes(sample = Aleatorio_usd_add))+
stat_qq() +
stat_qq_line()+
ggtitle("Aditiva - Dolares")+
labs(subtitle = paste("Prubas de Normalidad (Estadístico,P-Value): Shapiro Wilk:",round(sw_res_usd_add$statistic,3),",",round(sw_res_usd_add$p.value,4), "y",
"Jarque-Bera",round(jb_res_usd_add$statistic,3),",",round(jb_res_usd_add$p.value,4)))+
theme_bw()
p4<-ggplot(Aleatorio_Desc, aes(sample = Aleatorio_usd_multi))+
stat_qq() +
stat_qq_line()+
ggtitle("Multiplicativa - Dolares")+
labs(subtitle = paste("Prubas de Normalidad (Estadístico,P-Value): Shapiro Wilk:",round(sw_res_usd_multi$statistic,3),",",round(sw_res_usd_multi$p.value,4), "y",
"Jarque-Bera",round(jb_res_usd_multi$statistic,3),",",round(jb_res_usd_multi$p.value,4)))+
theme_bw()
grid.arrange(p1,p2,p3,p4,nrow=2, ncol = 2)
Costa Rica: QQPlot de los residuos de los saldos del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero en colones y dolares por tipo de descomposición, febrero-2001 a julio-2021
MaxLag2<-length(actnetcrc)/4
## Media Constante
### Niveles
# H0: No es estacionario
# H1: Es estacionario
adf_org_crc<-adf.test(actnetcrc,alternative="stationary") # Media no constante
adf_org_usd<-adf.test(actnetusd,alternative="stationary") # Media no constante
## Realiza la prueba de raíz unitaria de Zivot \ & Andrews, que permite una ruptura en un punto desconocido en la intersección, la tendencia lineal o en ambas.
## Esta prueba se basa en la estimación recursiva de una regresión de prueba. El estadístico de prueba se define como el estadístico t mínimo del coeficiente de la variable endógena rezagada.
## Recuérdese que en las pruebas a evaluar la hipótesis nula es presencia de raíz unitaria, mientras que la alternativa es estacionariedad.
## La prueba es muy sensible, realice pruebas y siempre daba resultados o pvalues diferente para una distribucion normal 1 , 0
za_org_crc<-ur.za(window(actnetcrc,start=c(2001,2),end=c(2020,2)), model="both")
summary(za_org_crc)##
## ################################
## # Zivot-Andrews Unit Root Test #
## ################################
##
##
## Call:
## lm(formula = testmat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -112020 -19964 -694 16741 158850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17396.37000 6617.37451 -2.629 0.00916 **
## y.l1 0.70943 0.04757 14.912 < 0.0000000000000002 ***
## trend 1094.74967 186.94371 5.856 0.0000000168 ***
## du 42188.76580 13230.73117 3.189 0.00163 **
## dt -980.19378 340.74430 -2.877 0.00441 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 37160 on 223 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.9805, Adjusted R-squared: 0.9801
## F-statistic: 2802 on 4 and 223 DF, p-value: < 0.00000000000000022
##
##
## Teststatistic: -6.1077
## Critical values: 0.01= -5.57 0.05= -5.08 0.1= -4.82
##
## Potential break point at position: 171plot(za_org_crc)
time(actnetcrc)[171]## [1] 2015.25za_org_usd<-ur.za(window(actnetusd,start=c(2001,2),end=c(2020,3)), model="both")
summary(za_org_usd)##
## ################################
## # Zivot-Andrews Unit Root Test #
## ################################
##
##
## Call:
## lm(formula = testmat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -171.049 -28.592 -2.375 22.788 219.846
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.48428 8.84633 -0.055 0.95639
## y.l1 0.81735 0.03686 22.173 < 0.0000000000000002 ***
## trend 1.02909 0.22921 4.490 0.0000114 ***
## du 45.46407 16.89895 2.690 0.00768 **
## dt -1.03127 0.34119 -3.023 0.00280 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53.66 on 224 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.9817, Adjusted R-squared: 0.9813
## F-statistic: 3000 on 4 and 224 DF, p-value: < 0.00000000000000022
##
##
## Teststatistic: -4.9551
## Critical values: 0.01= -5.57 0.05= -5.08 0.1= -4.82
##
## Potential break point at position: 155plot(za_org_usd)
time(actnetusd)[155]## [1] 2013.917En relación a la estacionariedad[^5] de las series, ambas no cumplen con dicha condición ya que presentan tendencia creciente y por ende no tienen media constante en el tiempo. Para confirmar esto realiza la prueba de hipótesis de Dickey-Fuller aumentada donde lo hipótesis nula es que la serie tiene raíz unitaria (proceso no estacionario), en ambos casos no se rechaza la hipótesis nula (Serie Colones: estadístico: -3.0082767 y valor-p: 0.1515055 y la Serie Dolares: estadístico: -2.7303393 y valor-p: 0.2684702), y se puede observar que la Función de Autocorrelación Simple Muestral (ACF) decae lentamente a 0 (Figuras @ref(fig:acfpacfseriescrc) y @ref(fig:acfpacfseriesusd)), esto sugiere que para realizar estacionaria las series se podrían transformar a logaritmo y diferenciar.
autocorrecrc<-acf2(actnetcrc,max.lag = MaxLag2)
Función de autocorrelación y autocorrelación parcial estimadas de la serie de cronológica de los saldos del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero en colones, febrero 2001 a diciembre-2020
#actnetcrc%>% ggtsdisplay(main="Colones")autocorreusd<-acf2(actnetcrc,max.lag = MaxLag2)Función de autocorrelación y autocorrelación parcial estimadas de la serie de cronológica de los saldos del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero en dolares, febrero 2001 a diciembre-2020
# actnetusd%>% ggtsdisplay(main="Dolares")crclineal<-fNonlinear::tnnTest(actnetusd, lag = 1, title = NULL, description = NULL)
usdlineal<-fNonlinear::tnnTest(actnetcrc, lag = 1, title = NULL, description = NULL)# Kennan tests for nonlineary
#
# La hipótesis nula de que la serie de tiempo sigue algún proceso de AR.
Keenan.test(log(actnetcrc))## $test.stat
## [1] 6.554213
##
## $p.value
## [1] 0.01107209
##
## $order
## [1] 1Keenan.test(log(actnetcrc), order=1)## $test.stat
## [1] 6.554213
##
## $p.value
## [1] 0.01107209
##
## $order
## [1] 1Keenan.test(log(actnetcrc), order=2)## $test.stat
## [1] 7.604942
##
## $p.value
## [1] 0.006268688
##
## $order
## [1] 2Keenan.test(log(actnetcrc), order=3)## $test.stat
## [1] 12.3357
##
## $p.value
## [1] 0.0005315724
##
## $order
## [1] 3Keenan.test(log(actnetusd))## $test.stat
## [1] 6.742342
##
## $p.value
## [1] 0.009991382
##
## $order
## [1] 1Keenan.test(log(actnetusd), order=1)## $test.stat
## [1] 6.742342
##
## $p.value
## [1] 0.009991382
##
## $order
## [1] 1Keenan.test(log(actnetusd), order=2)## $test.stat
## [1] 6.162359
##
## $p.value
## [1] 0.01373462
##
## $order
## [1] 2Keenan.test(log(actnetusd), order=3)## $test.stat
## [1] 7.098561
##
## $p.value
## [1] 0.008242026
##
## $order
## [1] 3Lo que respecta a la linealidad de las series, se observa que las mismas cumplen con la linealidad en la media lo que es confirmado con la prueba de hipótesis de Teraesvirta, de la cual se concluye que no hay suficiente evidencia estadística para rechazar la hipótesis nula que la serie cronológica es lineal en la media, tanto para colones como dolares (Colones: Estadístico 0.4947052 , Valor P 0.7808653 ; Estadístico 1.4958362 , Valor P 0.473351 )
En la figura @ref(fig:variabilidadseries) se observa para el caso de colones una variabilidad estable a lo largo del periodo de análisis, por otro lado, los saldos del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero en dolares se observa una variabilidad importante antes del año 2005, sin embargo, posterior a ese año tiende a estabilizarse.
variabilidad_crc <- log(actnetcrc)/log(stats::lag(actnetcrc,1))
variabilidad_usd <- log(actnetusd)/log(stats::lag(actnetusd,1))
pvariabilidad_crc<-autoplot(variabilidad_crc)+ theme_bw()+ ggtitle('Colones')+
scale_y_continuous(limits = c(0.75,1.1))
pvariabilidad_usd <- autoplot(variabilidad_usd)+ theme_bw()+ ggtitle('Dolares')+
scale_y_continuous(limits = c(0.75,1.1))
grid.arrange(pvariabilidad_crc,pvariabilidad_usd,nrow=1,ncol=2)
Evolución de la variabilidad de la serie cronológica de los saldos del Activo Neto Administrado de los Fondos de Inversión del Mercado de Dinero en dolares, febrero 2001 a diciembre-2020
3.2. Modelos
3.2.1. Modelo Lineal
A partir del análisis exploratorio realizado de las series y considerando sus caracteristicas se procedió a estimar 5 modelos de pronóstico lineales por cada serie:
Modelo de Suavizamiento Exponencial Holt-Winter Aditivo
Modelo de Suavizamiento Exponencial Holt-Winter Multiplicativo
3 Modelos univariantes autorregresivos integrados de media movil (ARIMA)
## Peridos de Tiempo
inicio_train<- c(2011,1)
fin_train<- c(2021,2)
inicio_test <- c(2021,3)
sactnetcrc<- window(actnetcrc,start=inicio_train)
sactnetcrc_train<- window(actnetcrc,start=inicio_train, end=fin_train)
sactnetcrc_test<- window(actnetcrc,start=inicio_test)
sactnetusd<- window(actnetusd,start=inicio_train)
sactnetusd_train<- window(actnetusd,start=inicio_train, end=fin_train)
sactnetusd_test<- window(actnetusd,start=inicio_test)
h.param <- length(sactnetcrc_test)Serie en Colones
Holt Winter
MODELOS
Holt Winter Multiplicativo
ht2_multi <- hw(sactnetcrc_train, seasonal = "multiplicative", h = h.param)
summary(ht2_multi)##
## Forecast method: Holt-Winters' multiplicative method
##
## Model Information:
## Holt-Winters' multiplicative method
##
## Call:
## hw(y = sactnetcrc_train, h = h.param, seasonal = "multiplicative")
##
## Smoothing parameters:
## alpha = 0.8493
## beta = 0.0001
## gamma = 0.0006
##
## Initial states:
## l = 396675.0123
## b = 5764.5991
## s = 0.8942 0.931 1.0095 1.0288 0.9945 1.0234
## 1.0383 1.0152 1.0124 0.988 1.0262 1.0384
##
## sigma: 0.0721
##
## AIC AICc BIC
## 3215.470 3221.355 3263.139
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set -1006.657 45159.96 35508.68 -0.540406 5.533052 0.377747 0.01506986
##
## Forecasts:
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Mar 2021 967725.8 878338.4 1057113 831019.5 1104432
## Apr 2021 997327.2 876620.2 1118034 812721.8 1181932
## May 2021 1005920.7 861127.4 1150714 784478.4 1227363
## Jun 2021 1034847.4 865609.1 1204086 776019.8 1293675
## Jul 2021 1026135.4 840499.1 1211772 742229.2 1310042pred_ht2_multi <- ht2_multi$mean
checkresiduals(ht2_multi)
##
## Ljung-Box test
##
## data: Residuals from Holt-Winters' multiplicative method
## Q* = 21.747, df = 8, p-value = 0.005407
##
## Model df: 16. Total lags used: 24Holt Winter Aditivo
ht2_add <- hw(sactnetcrc_train, seasonal = "additive", h = h.param)
summary(ht2_add)
pred_ht2_add <- ht2_add$mean
checkresiduals(ht2_add)
METRICAS HOLT WINTER
perf_ht2_add_train<-getPerformance(ht2_add$model$fitted,sactnetcrc_train)
perf_ht2_multi_train<-getPerformance(ht2_multi$model$fitted,sactnetcrc_train)
perf_ht2_add<-getPerformance(pred_ht2_add, sactnetcrc_test)
perf_ht2_multi<-getPerformance(pred_ht2_multi, sactnetcrc_test)
data.frame(
Modelo = c("Holt Winter Aditivia",
"Holt Winter Multiplicativa"),
AIC = c(ht2_add$model$aic,
ht2_multi$model$aic),
AICc= c(ht2_add$model$aicc,
ht2_multi$model$aicc),
BIC = c(ht2_add$model$bic,
ht2_multi$model$bic))%>%
arrange(AIC)%>%
knitr::kable(caption="Metricas de Bondad de Ajuste")
MetResHW<-cbind(
Modelo = c("Holt Winter Aditivia",
"Holt Winter Multiplicativa",
"Holt Winter Aditivia",
"Holt Winter Multiplicativa"),
Dataset=c("Entrenamiento","Entrenamiento","Prueba","Prueba"),
rbind(
perf_ht2_add_train,
perf_ht2_multi_train,
perf_ht2_add,
perf_ht2_multi
)
) %>%
arrange(RMSE)
MetResHW%>%
knitr::kable(caption="Metricas de Ajuste sobre la tabla de validación")ARIMA
##
## Call:
## seas(x = sactnetcrc_train, transform.function = "log", regression.aictest = NULL,
## outlier = NULL, regression.variables = "ao2020.Mar", arima.model = "(0 1 0)(1 0 1)")
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## AO2020.Mar -0.26213 0.04651 -5.636 0.0000000173759851 ***
## AR-Seasonal-12 0.93237 0.03143 29.664 < 0.0000000000000002 ***
## MA-Seasonal-12 0.62705 0.08332 7.526 0.0000000000000523 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## SEATS adj. ARIMA: (0 1 0)(1 0 1) Obs.: 122 Transform: log
## AICc: 2930, BIC: 2941 QS (no seasonality in final): 0
## Box-Ljung (no autocorr.): 25.52 Shapiro (normality): 0.9725 **##
## Call:
## seas(x = sactnetcrc_train, transform.function = "none", regression.aictest = NULL,
## outlier = NULL, regression.variables = "ao2020.Mar", arima.model = "(0 1 0)(0 1 1)")
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## AO2020.Mar -200519.71119 29678.44417 -6.756 0.0000000000141 ***
## MA-Seasonal-12 0.78410 0.07922 9.898 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## SEATS adj. ARIMA: (0 1 0)(0 1 1) Obs.: 122 Transform: none
## AICc: 2647, BIC: 2655 QS (no seasonality in final): 0
## Box-Ljung (no autocorr.): 27.41 Shapiro (normality): 0.9743 *##
## Call:
## seas(x = sactnetcrc_train, transform.function = "log", regression.aictest = NULL,
## outlier = NULL, regression.variables = c("ls2015.May", "ao2020.Mar"),
## arima.model = "(2 1 0)(1 0 0)")
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## LS2015.May 0.21302 0.05516 3.861 0.000113 ***
## AO2020.Mar -0.24242 0.04827 -5.023 0.00000051001064 ***
## AR-Nonseasonal-01 -0.13474 0.08772 -1.536 0.124556
## AR-Nonseasonal-02 -0.23542 0.08922 -2.639 0.008325 **
## AR-Seasonal-12 0.54195 0.07683 7.054 0.00000000000173 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## SEATS adj. ARIMA: (2 1 0)(1 0 0) Obs.: 122 Transform: log
## AICc: 2926, BIC: 2942 QS (no seasonality in final): 0
## Box-Ljung (no autocorr.): 19.38 Shapiro (normality): 0.9827Análisis de Supuestos
resseas1 <- resid(modelseas1)
resseas2 <- resid(modelseas2)
resseas3 <- resid(modelseas3)#### Estacionariedad de los residuos
## Media Constante
adf_res_CRC_1<- adf.test(resseas1 , alternative='stationary')
adf_res_CRC_2<- adf.test(resseas2 , alternative='stationary')
adf_res_CRC_3<- adf.test(resseas3 , alternative='stationary')
adf_res_CRC_1##
## Augmented Dickey-Fuller Test
##
## data: resseas1
## Dickey-Fuller = -4.8317, Lag order = 5, p-value = 0.01
## alternative hypothesis: stationaryadf_res_CRC_2##
## Augmented Dickey-Fuller Test
##
## data: resseas2
## Dickey-Fuller = -5.1753, Lag order = 4, p-value = 0.01
## alternative hypothesis: stationaryadf_res_CRC_3##
## Augmented Dickey-Fuller Test
##
## data: resseas3
## Dickey-Fuller = -4.4897, Lag order = 4, p-value = 0.01
## alternative hypothesis: stationary#### Autocorrelaciòn de los resiudos
#####################################
#Autocorrelacion de los residuales y pruebas gráficas
## Ljung-Box test
# H0: Independencia de los residuos
# H1: No Independencia de los residuos
lb_res_CRC_1 <- checkresiduals(modelseas1 , lag=MaxLag2)
lb_res_CRC_2 <- checkresiduals(modelseas2 , lag=MaxLag2)
lb_res_CRC_3 <- checkresiduals(modelseas3 , lag=MaxLag2)#### Varianza Constante de los residuos
## Varianza Constante ARCH Engle's Test for Residual Heteroscedasticity
# H0: los residuos son homocedasticos
# H1: los residuos no son homocedasticos
FinTS::ArchTest(resseas1,lag=12)##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: resseas1
## Chi-squared = 9.7695, df = 12, p-value = 0.6362FinTS::ArchTest(resseas2,lag=12)##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: resseas2
## Chi-squared = 12.286, df = 12, p-value = 0.423FinTS::ArchTest(resseas3,lag=12)##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: resseas3
## Chi-squared = 10.948, df = 12, p-value = 0.5334autoplot(resseas1^2 )+ theme_bw() ; acf2(resseas1^2, max.lag=MaxLag2)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
## ACF 0.11 0.12 -0.02 -0.10 -0.08 -0.16 -0.01 -0.07 -0.11 -0.11 -0.08 0.03
## PACF 0.11 0.11 -0.04 -0.11 -0.05 -0.13 0.03 -0.05 -0.12 -0.12 -0.06 0.03
## [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
## ACF 0.08 0.01 -0.03 -0.01 0.00 -0.06 0.00 0.02 -0.07 -0.03 -0.12 0.11
## PACF 0.07 -0.06 -0.11 -0.03 0.01 -0.07 -0.02 -0.03 -0.11 -0.03 -0.11 0.11
## [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36]
## ACF 0.03 0.04 0.13 0.01 0.01 -0.04 -0.06 0.01 0.00 0.03 -0.08 0.02
## PACF -0.01 -0.05 0.08 -0.02 -0.05 -0.02 -0.08 0.00 0.03 0.03 -0.09 0.03
## [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48]
## ACF -0.02 0.01 0.05 0.03 -0.04 -0.11 -0.08 -0.09 -0.03 -0.05 -0.01 -0.06
## PACF -0.04 0.02 0.05 -0.01 -0.12 -0.08 -0.05 -0.08 -0.01 -0.12 -0.06 -0.10
## [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60]
## ACF 0.00 0.02 0.05 0.26 0.18 0.15 0.06 -0.01 -0.02 -0.10 0.00 0.02
## PACF -0.04 -0.02 -0.06 0.16 0.08 0.05 0.05 -0.02 0.02 -0.04 0.08 0.07
## [,61]
## ACF -0.08
## PACF -0.05autoplot(resseas2^2 )+ theme_bw() ; acf2(resseas2^2, max.lag=MaxLag2)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## ACF 0.01 0.14 0.1 0.09 0.05 -0.02 0.22 -0.08 0.10 -0.03 -0.01 0.01 0.13
## PACF 0.01 0.14 0.1 0.07 0.02 -0.06 0.21 -0.08 0.05 -0.05 -0.04 0.01 0.17
## [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
## ACF -0.03 -0.05 0.08 0.06 0.00 0.00 0.04 -0.06 0.02 -0.11 0.14 -0.03
## PACF -0.09 -0.05 0.03 0.10 -0.01 -0.01 -0.05 -0.03 0.02 -0.11 0.16 -0.02
## [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
## ACF 0.15 0.15 -0.02 0.14 -0.04 0.01 -0.07 0.12 0.00 -0.07 0.04 -0.03
## PACF 0.14 0.19 -0.04 0.03 -0.04 -0.13 -0.02 0.05 -0.02 -0.04 0.02 -0.03
## [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49]
## ACF 0.02 -0.06 0.11 -0.02 -0.02 -0.06 -0.07 -0.01 -0.09 -0.04 -0.06 -0.03
## PACF 0.07 -0.04 0.05 0.02 -0.07 -0.13 -0.05 -0.02 -0.09 0.03 0.01 0.03
## [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61]
## ACF 0.02 -0.01 0.01 0.11 0.14 0.02 0.06 0.02 -0.07 0.03 0.01 -0.04
## PACF 0.11 -0.03 0.03 0.05 0.12 0.00 0.04 -0.10 -0.14 0.08 -0.02 -0.03autoplot(resseas3^2 )+ theme_bw() ; acf2(resseas3^2, max.lag=MaxLag2)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## ACF 0.02 0.11 0.03 -0.02 -0.13 -0.14 -0.02 -0.09 -0.03 0.06 0.07 0.21 0.05
## PACF 0.02 0.11 0.02 -0.03 -0.14 -0.14 0.02 -0.05 -0.03 0.06 0.05 0.20 0.02
## [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
## ACF 0.10 0.00 -0.04 0.00 -0.08 0.12 -0.08 0.06 0.01 0.01 0.11 -0.05
## PACF 0.03 -0.02 -0.03 0.06 -0.01 0.16 -0.04 0.04 0.00 -0.03 0.09 -0.06
## [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
## ACF 0.00 -0.08 -0.08 0.14 -0.12 -0.02 -0.10 0.04 -0.10 -0.09 0.10 -0.06
## PACF -0.05 -0.05 -0.07 0.20 -0.11 -0.13 -0.12 -0.01 -0.05 -0.12 0.03 -0.04
## [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49]
## ACF 0.01 -0.06 0.10 0.10 -0.05 0.10 -0.06 -0.03 -0.09 -0.08 -0.09 -0.09
## PACF -0.02 -0.04 0.06 0.11 -0.04 0.05 0.03 0.02 0.03 -0.03 -0.15 0.04
## [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61]
## ACF -0.05 -0.10 0.04 0.05 -0.04 0.20 -0.05 0.01 -0.04 -0.04 -0.05 -0.07
## PACF -0.07 -0.05 -0.01 -0.05 -0.06 0.15 -0.04 -0.07 -0.04 -0.03 0.01 0.05#### Normalidad de los residuos
#####################################
#Normalidad de los residuales
# H0: La muestra proviene de una distribución normal.
# H1: La muestra no proviene de una distribución normal.
## Jarque Bera
jb_res_CRC_1<-jarque.bera.test(resseas1) # Cumple
jb_res_CRC_2<-jarque.bera.test(resseas2) # Cumple
jb_res_CRC_3<-jarque.bera.test(resseas3) # Cumple
jb_res_CRC_1##
## Jarque Bera Test
##
## data: resseas1
## X-squared = 9.9942, df = 2, p-value = 0.006758jb_res_CRC_2##
## Jarque Bera Test
##
## data: resseas2
## X-squared = 4.636, df = 2, p-value = 0.09847jb_res_CRC_3##
## Jarque Bera Test
##
## data: resseas3
## X-squared = 1.8213, df = 2, p-value = 0.4023sw_res_CRC_1<-shapiro.test(resseas1) # Cumple
sw_res_CRC_2<-shapiro.test(resseas2) # Cumple
sw_res_CRC_3<-shapiro.test(resseas3) # Cumple
sw_res_CRC_1##
## Shapiro-Wilk normality test
##
## data: resseas1
## W = 0.97246, p-value = 0.008379sw_res_CRC_2##
## Shapiro-Wilk normality test
##
## data: resseas2
## W = 0.97432, p-value = 0.02051sw_res_CRC_3##
## Shapiro-Wilk normality test
##
## data: resseas3
## W = 0.98269, p-value = 0.1232# car::qqPlot(resseas1)
# car::qqPlot(resseas2)
# car::qqPlot(resseas3)#### PRONOSTICO SOBRE LOS DATOS DE VALIDACION
pronostico_CRC_1 <-
window(
series(modelseas1, "forecast.forecasts"),
start = inicio_test,
end = c(2021, 7)
)
pronostico_CRC_2 <-
window(
series(modelseas2, "forecast.forecasts"),
start = inicio_test,
end = c(2021, 7)
)
pronostico_CRC_3 <-
window(
series(modelseas3, "forecast.forecasts"),
start = inicio_test,
end = c(2021, 7)
)
## PRONOSTICO SOBRE ENTRENAMIENTO
pronostico_CRC_1_train <- final(modelseas1)
pronostico_CRC_2_train <- final(modelseas2)
pronostico_CRC_3_train <- final(modelseas3)### METRICAS DE RENDIMIENTO ARIMA
## ENTRENAMIENTO
perfor_crc_train_mod_1 <-
getPerformance(pronostico_CRC_1_train, sactnetcrc_train)
perfor_crc_train_mod_2 <-
getPerformance(pronostico_CRC_2_train, sactnetcrc_train)
perfor_crc_train_mod_3 <-
getPerformance(pronostico_CRC_3_train, sactnetcrc_train)
### VALIDACION
###
perfor_crc_test_mod_1 <-
getPerformance(pronostico_CRC_1[, 1], sactnetcrc_test)
perfor_crc_test_mod_2 <-
getPerformance(pronostico_CRC_2[, 1], sactnetcrc_test)
perfor_crc_test_mod_3 <-
getPerformance(pronostico_CRC_3[, 1], sactnetcrc_test)
### TABLA DE METRICAS
## ENTRENAMIENTO (AIC,BIC)
data.frame(
Models=c(
"1. ARIMA (0 1 0)(1 0 1) Log",
"2. ARIMA (0 1 0)(0 1 1) Niveles",
"3. ARIMA (2 1 0)(1 0 0) Log"
),
AIC=c(AIC(modelseas1),AIC(modelseas2),AIC(modelseas3)),
BIC=c(BIC(modelseas1),BIC(modelseas2),BIC(modelseas3))
)%>%
arrange(AIC)%>%
knitr::kable(caption="Medidas de Ajuste: ARIMA Entrenamiento")| Models | AIC | BIC |
|---|---|---|
| 2. ARIMA (0 1 0)(0 1 1) Niveles | 2646.710 | 2654.784 |
| 3. ARIMA (2 1 0)(1 0 0) Log | 2925.722 | 2942.497 |
| 1. ARIMA (0 1 0)(1 0 1) Log | 2929.554 | 2940.737 |
## VALIDACION Y ENTRENAMIENTO
Metricas_Sarima_CRC <- data.frame(
Modelo = rep(
c(
"1. ARIMA (0 1 0)(1 0 1) Log",
"2. ARIMA (0 1 0)(0 1 1) Niveles",
"3. ARIMA (2 1 0)(1 0 0) Log"
),
2
),
Dataset = c(rep("Entrenamiento", 3), rep("Prueba", 3)),
rbind(
perfor_crc_train_mod_1,
perfor_crc_train_mod_2,
perfor_crc_train_mod_3,
perfor_crc_test_mod_1,
perfor_crc_test_mod_2,
perfor_crc_test_mod_3
)
)
#Metricas_Mod_Lin <- bind_rows(Metricas_HW, Metricas_Sarima_CRC)
Metricas_Mod_Lin<- Metricas_Sarima_CRC
knitr::kable(Metricas_Mod_Lin)| Modelo | Dataset | MAE | RSS | MSE | RMSE |
|---|---|---|---|---|---|
| 1. ARIMA (0 1 0)(1 0 1) Log | Entrenamiento | 21165.00 | 99913636070 | 818964230 | 28617.55 |
| 2. ARIMA (0 1 0)(0 1 1) Niveles | Entrenamiento | 21829.20 | 103361143650 | 847222489 | 29107.09 |
| 3. ARIMA (2 1 0)(1 0 0) Log | Entrenamiento | 28923.01 | 165071131447 | 1353042061 | 36783.72 |
| 1. ARIMA (0 1 0)(1 0 1) Log | Prueba | 47783.00 | 17050515820 | 3410103164 | 58396.09 |
| 2. ARIMA (0 1 0)(0 1 1) Niveles | Prueba | 47704.12 | 17563051551 | 3512610310 | 59267.28 |
| 3. ARIMA (2 1 0)(1 0 0) Log | Prueba | 42671.15 | 12081978350 | 2416395670 | 49156.85 |
# ggplot(Metricas_Mod_Lin) +
# aes(x = Modelo, fill = Dataset, weight = RMSE) +
# geom_bar() +
# scale_fill_manual(values = c(Entrenamiento = "#E69999",
# Prueba = "#5C7FA7")) +
# labs(x = "Método", y = "RMSE") +
# coord_flip() +
# theme_minimal() +
# theme(legend.position = "none") +
# facet_wrap(vars(Dataset), scales = "free", ncol = 1L)Métricas de Rendimiento Modelos Lineales
rbind(Metricas_Sarima_CRC,
MetResHW)%>%
arrange(Dataset,MAE)%>%
knitr::kable(caption="Metricas de Rendimiento sobre la muestra de prueba")| Modelo | Dataset | MAE | RSS | MSE | RMSE |
|---|---|---|---|---|---|
| 1. ARIMA (0 1 0)(1 0 1) Log | Entrenamiento | 21165.00 | 99913636070 | 818964230 | 28617.55 |
| 2. ARIMA (0 1 0)(0 1 1) Niveles | Entrenamiento | 21829.20 | 103361143650 | 847222489 | 29107.09 |
| 3. ARIMA (2 1 0)(1 0 0) Log | Entrenamiento | 28923.01 | 165071131447 | 1353042061 | 36783.72 |
| Holt Winter Aditivia | Entrenamiento | 32997.01 | 230557903869 | 1889818884 | 43472.05 |
| Holt Winter Multiplicativa | Entrenamiento | 35508.68 | 248809460176 | 2039421805 | 45159.96 |
| 3. ARIMA (2 1 0)(1 0 0) Log | Prueba | 42671.15 | 12081978350 | 2416395670 | 49156.85 |
| 2. ARIMA (0 1 0)(0 1 1) Niveles | Prueba | 47704.12 | 17563051551 | 3512610310 | 59267.28 |
| 1. ARIMA (0 1 0)(1 0 1) Log | Prueba | 47783.00 | 17050515820 | 3410103164 | 58396.09 |
| Holt Winter Aditivia | Prueba | 47797.04 | 16671689420 | 3334337884 | 57743.73 |
| Holt Winter Multiplicativa | Prueba | 53117.50 | 18279523374 | 3655904675 | 60464.08 |
### Holt-Winter Aditivo
# ht2_add_all <- hw(sactnetcrc,seasonal="additive",h = 5)
# autoplot(ht2_add_all)+
# theme_bw()
### Arima (2,1,0) (1,0,1) Log
modelseas2_all <- seas(
x = sactnetcrc,
transform.function = "none",
regression.aictest = NULL,
outlier = NULL,
regression.variables = "ao2020.Mar",
arima.model = "(0 1 0)(0 1 1)"
)
pronostico_lin <- window(
series(modelseas2_all, "forecast.forecasts"),
start = c(2021, 8),
end = c(2021, 12)
)
autoplot(sactnetcrc) +
autolayer(pronostico_lin) +
theme_bw()
Serie en Dolares
Hold-Winter
Holt Winter Multiplicativo
ht2_multi_usd <- hw(sactnetusd_train,seasonal="multiplicative",h = h.param)
summary(ht2_multi_usd)##
## Forecast method: Holt-Winters' multiplicative method
##
## Model Information:
## Holt-Winters' multiplicative method
##
## Call:
## hw(y = sactnetusd_train, h = h.param, seasonal = "multiplicative")
##
## Smoothing parameters:
## alpha = 0.7168
## beta = 0.0001
## gamma = 0.0006
##
## Initial states:
## l = 654.1817
## b = 10.5878
## s = 0.9495 0.9737 0.9555 0.9715 1.0324 1.032
## 1.0286 1.0576 0.9948 1.0028 1.0128 0.9889
##
## sigma: 0.0772
##
## AIC AICc BIC
## 1658.563 1664.447 1706.231
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE ACF1
## Training set -3.323565 70.01249 54.89003 -0.8501555 5.724199 0.3219086 0.172628
##
## Forecasts:
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Mar 2021 1656.303 1492.375 1820.230 1405.598 1907.008
## Apr 2021 1653.377 1452.261 1854.492 1345.797 1960.956
## May 2021 1768.911 1520.310 2017.512 1388.709 2149.113
## Jun 2021 1731.074 1459.224 2002.923 1315.316 2146.832
## Jul 2021 1747.492 1447.202 2047.783 1288.237 2206.747pred_ht2_multi_usd <- ht2_multi_usd$mean
checkresiduals(ht2_multi_usd)
##
## Ljung-Box test
##
## data: Residuals from Holt-Winters' multiplicative method
## Q* = 22.92, df = 8, p-value = 0.003468
##
## Model df: 16. Total lags used: 24Holt Winter Aditivo
ht2_add_usd <- hw(sactnetusd_train,seasonal="additive",h = h.param)
summary(ht2_add_usd)
pred_ht2_add_usd <- ht2_add_usd$mean
checkresiduals(ht2_add_usd)
METRICAS HOLT WINTER
perf_ht2_add_train_usd<-getPerformance(ht2_add_usd$model$fitted,sactnetusd_train)
perf_ht2_multi_train_usd<-getPerformance(ht2_multi_usd$model$fitted,sactnetusd_train)
perf_ht2_add_usd<-getPerformance(pred_ht2_add_usd, sactnetusd_test)
perf_ht2_multi_usd<-getPerformance(pred_ht2_multi_usd, sactnetusd_test)
data.frame(
Modelo = c("Holt Winter Aditivia",
"Holt Winter Multiplicativa"),
AIC = c(ht2_add_usd$model$aic,
ht2_multi_usd$model$aic),
AICc= c(ht2_add_usd$model$aicc,
ht2_multi_usd$model$aicc),
BIC = c(ht2_add_usd$model$bic,
ht2_multi_usd$model$bic))%>%
arrange(AIC)%>%
knitr::kable(caption="Metricas de Bondad de Ajuste")
MetResHW_usd<-cbind(
Modelo = c("Holt Winter Aditivia",
"Holt Winter Multiplicativa",
"Holt Winter Aditivia",
"Holt Winter Multiplicativa"),
Dataset=c("Entrenamiento","Entrenamiento","Prueba","Prueba"),
rbind(
perf_ht2_add_train_usd,
perf_ht2_multi_train_usd,
perf_ht2_add_usd,
perf_ht2_multi_usd
)
) %>%
arrange(RMSE)
MetResHW_usd%>%
knitr::kable(caption="Metricas de Ajuste sobre la tabla de validación")ARIMA
##
## Call:
## seas(x = sactnetcrc_train, transform.function = "log", regression.aictest = NULL,
## outlier = NULL, regression.variables = "ao2020.Mar", arima.model = "(0 1 0)(1 0 1)")
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## AO2020.Mar -0.26213 0.04651 -5.636 0.0000000173759851 ***
## AR-Seasonal-12 0.93237 0.03143 29.664 < 0.0000000000000002 ***
## MA-Seasonal-12 0.62705 0.08332 7.526 0.0000000000000523 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## SEATS adj. ARIMA: (0 1 0)(1 0 1) Obs.: 122 Transform: log
## AICc: 2930, BIC: 2941 QS (no seasonality in final): 0
## Box-Ljung (no autocorr.): 25.52 Shapiro (normality): 0.9725 **##
## Call:
## seas(x = sactnetcrc_train, transform.function = "none", regression.aictest = NULL,
## outlier = NULL, regression.variables = "ao2020.Mar", arima.model = "(0 1 0)(0 1 1)")
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## AO2020.Mar -200519.71119 29678.44417 -6.756 0.0000000000141 ***
## MA-Seasonal-12 0.78410 0.07922 9.898 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## SEATS adj. ARIMA: (0 1 0)(0 1 1) Obs.: 122 Transform: none
## AICc: 2647, BIC: 2655 QS (no seasonality in final): 0
## Box-Ljung (no autocorr.): 27.41 Shapiro (normality): 0.9743 *##
## Call:
## seas(x = sactnetcrc_train, transform.function = "log", regression.aictest = NULL,
## outlier = NULL, regression.variables = c("ls2015.May", "ao2020.Mar"),
## arima.model = "(2 1 0)(1 0 0)")
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## LS2015.May 0.21302 0.05516 3.861 0.000113 ***
## AO2020.Mar -0.24242 0.04827 -5.023 0.00000051001064 ***
## AR-Nonseasonal-01 -0.13474 0.08772 -1.536 0.124556
## AR-Nonseasonal-02 -0.23542 0.08922 -2.639 0.008325 **
## AR-Seasonal-12 0.54195 0.07683 7.054 0.00000000000173 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## SEATS adj. ARIMA: (2 1 0)(1 0 0) Obs.: 122 Transform: log
## AICc: 2926, BIC: 2942 QS (no seasonality in final): 0
## Box-Ljung (no autocorr.): 19.38 Shapiro (normality): 0.9827Análisis de Supuestos
resseas1_usd<-resid(modelseas1_usd)
resseas2_usd<-resid(modelseas2_usd)
resseas3_usd<-resid(modelseas3_usd)#### Estacionariedad de los residuos
## Media Constante
adf_res_usd_1<- adf.test(resseas1_usd , alternative='stationary')
adf_res_usd_2<- adf.test(resseas2_usd , alternative='stationary')
adf_res_usd_3<- adf.test(resseas3_usd , alternative='stationary')
adf_res_usd_1##
## Augmented Dickey-Fuller Test
##
## data: resseas1_usd
## Dickey-Fuller = -4.83, Lag order = 4, p-value = 0.01
## alternative hypothesis: stationaryadf_res_usd_2##
## Augmented Dickey-Fuller Test
##
## data: resseas2_usd
## Dickey-Fuller = -4.9356, Lag order = 4, p-value = 0.01
## alternative hypothesis: stationaryadf_res_usd_3##
## Augmented Dickey-Fuller Test
##
## data: resseas3_usd
## Dickey-Fuller = -5.34, Lag order = 4, p-value = 0.01
## alternative hypothesis: stationary#### Autocorrelaciòn de los resiudos
#####################################
#Autocorrelacion de los residuales y pruebas gráficas
## Ljung-Box test
# H0: Independencia de los residuos
# H1: No Independencia de los residuos
lb_res_usd_1 <- checkresiduals(modelseas1_usd , lag=MaxLag2)
lb_res_usd_2 <- checkresiduals(modelseas2_usd , lag=MaxLag2)
lb_res_usd_3 <- checkresiduals(modelseas3_usd , lag=MaxLag2)
#####################################
#Normalidad de los residuales
# H0: La muestra proviene de una distribución normal.
# H1: La muestra no proviene de una distribución normal.
## Jarque Bera
jb_res_usd_1<-jarque.bera.test(resseas1_usd) # Cumple
jb_res_usd_2<-jarque.bera.test(resseas2_usd) # Cumple
jb_res_usd_3<-jarque.bera.test(resseas3_usd) # Cumple
jb_res_usd_1##
## Jarque Bera Test
##
## data: resseas1_usd
## X-squared = 0.54286, df = 2, p-value = 0.7623jb_res_usd_2##
## Jarque Bera Test
##
## data: resseas2_usd
## X-squared = 0.45663, df = 2, p-value = 0.7959jb_res_usd_3##
## Jarque Bera Test
##
## data: resseas3_usd
## X-squared = 1.0719, df = 2, p-value = 0.5851sw_res_usd_1<-shapiro.test(resseas1_usd) # Cumple
sw_res_usd_2<-shapiro.test(resseas2_usd) # Cumple
sw_res_usd_3<-shapiro.test(resseas3_usd) # Cumple
sw_res_usd_1##
## Shapiro-Wilk normality test
##
## data: resseas1_usd
## W = 0.99011, p-value = 0.6078sw_res_usd_2##
## Shapiro-Wilk normality test
##
## data: resseas2_usd
## W = 0.99379, p-value = 0.8693sw_res_usd_3##
## Shapiro-Wilk normality test
##
## data: resseas3_usd
## W = 0.98456, p-value = 0.1791car::qqPlot(resseas1_usd)
## [1] 44 73car::qqPlot(resseas2_usd)
## [1] 29 56car::qqPlot(resseas3_usd)
## [1] 56 113#### Varianza Constante de los residuos
## Varianza Constante ARCH Engle's Test for Residual Heteroscedasticity
# H0: los residuos son homocedasticos
# H1: los residuos no son homocedasticos
FinTS::ArchTest(resseas1_usd,lag=12)##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: resseas1_usd
## Chi-squared = 8.6313, df = 12, p-value = 0.7341FinTS::ArchTest(resseas2_usd,lag=12)##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: resseas2_usd
## Chi-squared = 9.3079, df = 12, p-value = 0.6764FinTS::ArchTest(resseas3_usd,lag=12)##
## ARCH LM-test; Null hypothesis: no ARCH effects
##
## data: resseas3_usd
## Chi-squared = 7.3322, df = 12, p-value = 0.8349autoplot(resseas1_usd^2 )+ theme_bw(); acf2(resseas1_usd^2, max.lag=MaxLag2)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## ACF -0.08 0.07 0.09 -0.12 -0.10 0 -0.01 -0.04 -0.10 0.02 -0.10 0.01 0.14
## PACF -0.08 0.07 0.10 -0.12 -0.14 0 0.04 -0.03 -0.15 -0.01 -0.06 0.01 0.12
## [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
## ACF -0.03 0.15 -0.15 -0.03 -0.04 -0.09 -0.11 0.04 -0.01 0.07 -0.05 0.06
## PACF -0.03 0.11 -0.18 -0.04 -0.03 -0.04 -0.15 0.00 0.04 0.08 -0.07 -0.04
## [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
## ACF 0.01 0.10 -0.03 0.07 0.02 -0.06 0.08 0.05 -0.06 0.06 -0.04 -0.09
## PACF 0.03 0.11 -0.11 0.04 0.02 0.02 0.08 0.09 -0.03 0.04 -0.10 -0.05
## [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49]
## ACF -0.09 -0.03 -0.03 -0.08 0.12 -0.10 0.09 0.06 -0.03 0.04 0.05 -0.09
## PACF -0.11 -0.02 -0.08 -0.04 0.08 -0.02 0.05 0.03 -0.01 0.01 0.00 -0.04
## [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61]
## ACF 0.09 -0.04 0.04 -0.04 -0.08 0.01 0.03 0.14 0.09 0.10 -0.07 -0.09
## PACF 0.08 0.06 0.06 0.00 -0.14 -0.03 0.06 0.10 0.06 0.07 -0.12 -0.06autoplot(resseas2_usd^2 )+ theme_bw(); acf2(resseas2_usd^2, max.lag=MaxLag2)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## ACF -0.14 0.10 0.03 -0.01 -0.02 0.12 -0.14 0.07 -0.09 0.01 0.09 -0.11 0.03
## PACF -0.14 0.09 0.06 -0.01 -0.03 0.12 -0.10 0.02 -0.07 -0.01 0.11 -0.10 0.02
## [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
## ACF -0.05 0.00 -0.11 -0.08 -0.07 -0.03 -0.05 0.06 -0.02 -0.01 -0.04 0.08
## PACF -0.06 0.03 -0.13 -0.13 -0.04 -0.06 -0.01 0.02 0.04 -0.01 -0.08 0.08
## [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
## ACF -0.04 0.15 -0.12 0.12 -0.05 0.02 -0.03 0.16 -0.01 0.15 -0.05 0.00
## PACF -0.05 0.16 -0.12 0.09 -0.03 -0.03 -0.03 0.08 0.10 0.06 -0.03 -0.04
## [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49]
## ACF 0.03 0.00 -0.06 -0.03 0.02 -0.09 0.05 -0.10 0 -0.11 0.09 -0.12
## PACF 0.02 0.03 -0.13 -0.02 0.05 -0.03 0.00 -0.08 0 -0.11 0.06 -0.08
## [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61]
## ACF 0.01 -0.06 0.06 -0.06 0.04 -0.05 0.12 0.02 -0.06 0.14 -0.05 -0.05
## PACF -0.01 0.05 0.03 0.01 -0.06 0.01 0.07 0.08 -0.10 0.11 -0.07 -0.08autoplot(resseas3_usd^2 )+ theme_bw(); acf2(resseas3_usd^2, max.lag=MaxLag2)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## ACF -0.12 0.12 0.00 -0.04 -0.08 0.03 -0.06 0.01 -0.04 0.01 0.04 -0.12 0.11
## PACF -0.12 0.11 0.03 -0.06 -0.09 0.03 -0.03 -0.01 -0.04 0.00 0.05 -0.13 0.08
## [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
## ACF -0.11 0.05 -0.09 -0.06 -0.05 -0.02 -0.07 0.06 0.05 0.04 -0.07 0.11
## PACF -0.07 0.03 -0.09 -0.10 -0.03 -0.03 -0.05 0.01 0.08 0.04 -0.12 0.11
## [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
## ACF -0.01 0.08 -0.04 0.13 -0.06 0.03 0.04 0.12 0.00 0.13 0 -0.03
## PACF 0.01 0.11 -0.08 0.12 -0.01 0.00 0.05 0.14 0.04 0.10 0 0.02
## [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49]
## ACF 0.00 -0.05 -0.04 -0.06 0.06 -0.10 0.03 -0.11 0.00 -0.12 0.07 -0.15
## PACF -0.01 0.04 -0.08 0.02 0.05 -0.04 -0.03 -0.06 -0.01 -0.09 -0.01 -0.09
## [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61]
## ACF 0.04 -0.06 0.07 -0.08 0.03 -0.01 0.05 0.17 -0.01 0.15 -0.08 0.02
## PACF -0.01 -0.03 0.03 -0.05 -0.06 -0.04 0.03 0.16 -0.04 0.13 -0.09 -0.03### Performance de los modelos
pronostico_usd_1 <- window(series(modelseas1_usd,"forecast.forecasts"),start=inicio_test,end=c(2021,7))
pronostico_usd_2 <- window(series(modelseas2_usd,"forecast.forecasts"),start=inicio_test,end=c(2021,7))
pronostico_usd_3 <- window(series(modelseas3_usd,"forecast.forecasts"),start=inicio_test,end=c(2021,7))
pronostico_usd_1_train <- final(modelseas1_usd)
pronostico_usd_2_train <- final(modelseas2_usd)
pronostico_usd_3_train <- final(modelseas3_usd) perfor_usd_train_mod_1 <- getPerformance(pronostico_usd_1_train, sactnetusd_train)
perfor_usd_train_mod_2 <- getPerformance(pronostico_usd_2_train, sactnetusd_train)
perfor_usd_train_mod_3 <- getPerformance(pronostico_usd_3_train, sactnetusd_train)
perfor_usd_test_mod_1 <- getPerformance(pronostico_usd_1[,1],sactnetusd_test)
perfor_usd_test_mod_2 <- getPerformance(pronostico_usd_2[,1],sactnetusd_test)
perfor_usd_test_mod_3 <- getPerformance(pronostico_usd_3[,1],sactnetusd_test)data.frame(
Models=c(
"1. ARIMA (0 1 0)(1 0 1) Log",
"2. ARIMA (0 1 0)(0 1 1) Niveles",
"3. ARIMA (2 1 0)(1 0 0) Log"
),
AIC=c(AIC(modelseas1_usd),AIC(modelseas2_usd),AIC(modelseas3_usd)),
BIC=c(BIC(modelseas1_usd),BIC(modelseas2_usd),BIC(modelseas3_usd))
)%>%
arrange(AIC)%>%
knitr::kable(caption="Medidas de Ajuste: ARIMA Entrenamiento")| Models | AIC | BIC |
|---|---|---|
| 3. ARIMA (2 1 0)(1 0 0) Log | 1272.902 | 1280.976 |
| 2. ARIMA (0 1 0)(0 1 1) Niveles | 1282.902 | 1290.976 |
| 1. ARIMA (0 1 0)(1 0 1) Log | 1299.884 | 1307.958 |
Metricas_Sarima_usd <- data.frame(
Modelo = rep(
c(
"1.ARIMA (0 1 1)(1 1 0) Niveles",
"2.ARIMA (0 1 1)(0 1 1) Log",
"3.ARIMA (0 1 1)(0 1 1) Niveles"
),
2
),
Dataset = c(rep("Entrenamiento", 3), rep("Prueba", 3)),
rbind(
perfor_usd_train_mod_1,
perfor_usd_train_mod_2,
perfor_usd_train_mod_3,
perfor_usd_test_mod_1,
perfor_usd_test_mod_2,
perfor_usd_test_mod_3
)
)
#Metricas_Mod_Lin<- bind_rows(Metricas_HW_usd,Metricas_Sarima_usd)
Metricas_Mod_Lin<- Metricas_Sarima_usd
Metricas_Mod_Lin%>%
knitr::kable()| Modelo | Dataset | MAE | RSS | MSE | RMSE |
|---|---|---|---|---|---|
| 1.ARIMA (0 1 1)(1 1 0) Niveles | Entrenamiento | 44.26093 | 360934.60 | 2958.480 | 54.39191 |
| 2.ARIMA (0 1 1)(0 1 1) Log | Entrenamiento | 40.61693 | 312328.78 | 2560.072 | 50.59715 |
| 3.ARIMA (0 1 1)(0 1 1) Niveles | Entrenamiento | 38.43133 | 255606.93 | 2095.139 | 45.77269 |
| 1.ARIMA (0 1 1)(1 1 0) Niveles | Prueba | 35.96765 | 12560.82 | 2512.163 | 50.12148 |
| 2.ARIMA (0 1 1)(0 1 1) Log | Prueba | 38.93622 | 10896.37 | 2179.274 | 46.68270 |
| 3.ARIMA (0 1 1)(0 1 1) Niveles | Prueba | 62.33028 | 22993.34 | 4598.669 | 67.81348 |
Métricas de Rendimiento Modelos Lineales
rbind(Metricas_Sarima_usd,
MetResHW_usd)%>%
arrange(Dataset,MAE)%>%
knitr::kable(caption="Metricas de Rendimiento sobre la muestra de prueba")| Modelo | Dataset | MAE | RSS | MSE | RMSE |
|---|---|---|---|---|---|
| 3.ARIMA (0 1 1)(0 1 1) Niveles | Entrenamiento | 38.43133 | 255606.93 | 2095.139 | 45.77269 |
| 2.ARIMA (0 1 1)(0 1 1) Log | Entrenamiento | 40.61693 | 312328.78 | 2560.072 | 50.59715 |
| 1.ARIMA (0 1 1)(1 1 0) Niveles | Entrenamiento | 44.26093 | 360934.60 | 2958.480 | 54.39191 |
| Holt Winter Aditivia | Entrenamiento | 53.54778 | 563335.58 | 4617.505 | 67.95222 |
| Holt Winter Multiplicativa | Entrenamiento | 54.89003 | 598013.39 | 4901.749 | 70.01249 |
| 1.ARIMA (0 1 1)(1 1 0) Niveles | Prueba | 35.96765 | 12560.82 | 2512.163 | 50.12148 |
| 2.ARIMA (0 1 1)(0 1 1) Log | Prueba | 38.93622 | 10896.37 | 2179.274 | 46.68270 |
| Holt Winter Aditivia | Prueba | 53.12659 | 16432.39 | 3286.479 | 57.32782 |
| Holt Winter Multiplicativa | Prueba | 54.05022 | 18804.05 | 3760.810 | 61.32544 |
| 3.ARIMA (0 1 1)(0 1 1) Niveles | Prueba | 62.33028 | 22993.34 | 4598.669 | 67.81348 |
### Holt-Winter MULTIPLICATUVI
# ht2_multi_all <- hw(sactnetusd,seasonal="multiplicative",h = 5)
# autoplot(ht2_multi_all)+
# theme_bw()
modelseas2_all<-seas(
x = sactnetusd,
transform.function = "log",
regression.aictest = NULL,
outlier = NULL,
arima.model = "(0 1 1)(0 1 1)"
)
autoplot(sactnetusd)+
autolayer(window(series(modelseas2_all,"forecast.forecasts"),start=c(2021,8),end=c(2021,12)))+
theme_bw()
3.2.2. Modelo No Lineal
Serie en Colones
TAR
Definir Parametros modelo TAR
# m orden
pm <- 1:3
mod.list.tar<-list()
AIC.best.list<-list()
AICM = NULL
model.best <- list(d=0, p1=0, p2=0)
AIC.best = 2964
for(l in pm){
for(j in pm){
for(i in pm){
set.seed(777)
model.tar.s = tar(sactnetcrc_train,p1=j,p2=i,d=l)
mod.list.tar[[paste(j,i,l,sep="-")]]<-model.tar.s$AIC
#print(paste(j,i,l,model.tar.s$AIC,sep="-"))
if (model.tar.s$AIC < AIC.best) {
AIC.best = model.tar.s$AIC
AIC.best.list[[paste(j,i,l,sep="-")]]<-AIC.best
#print(AIC.best)
model.best$d = l
model.best$p1 = model.tar.s$p1
model.best$p2 = model.tar.s$p2
print(paste(model.tar.s$p1,model.tar.s$p2,l,sep="-")) }
}
}
}## [1] "0-3-1"
## [1] "3-1-1"
## [1] "3-3-1"
## [1] "3-1-2"
## [1] "3-2-2"# AICTar<-bind_rows(mod.list.tar,.id = "Ordene-delay")%>%
# arrange(`1`)
#
# knitr::kable(head(AICTar,20))
AICTarBest<-bind_rows(AIC.best.list,.id = "Ordene-delay")%>%
arrange(`1`)
knitr::kable(head(AICTarBest,20))| Ordene-delay | 1 |
|---|---|
| 3-2-2 | 2924 |
| 3-1-2 | 2925 |
| 3-3-1 | 2929 |
| 3-1-1 | 2930 |
| 1-3-1 | 2936 |
Los tres mejores modelos
mod.tar1<-TSA::tar(sactnetcrc_train,p1=3,p2=2,d=1)
mod.tar2<-TSA::tar(sactnetcrc_train,p1=3,p2=1,d=2)
mod.tar3<-TSA::tar(sactnetcrc_train,p1=3,p2=3,d=1)
mod.tar1$thd##
## 634783.1mod.tar2$thd##
## 733818.8mod.tar3$thd##
## 511224.5mod.tar1$qr1$coefficients## intercept-sactnetcrc_train lag1-sactnetcrc_train
## 21868.4352666 0.7499834
## lag2-sactnetcrc_train lag3-sactnetcrc_train
## -0.3430847 0.5609114mod.tar2$qr1$coefficients## intercept-sactnetcrc_train lag1-sactnetcrc_train
## 15867.9606105 0.8874530
## lag2-sactnetcrc_train lag3-sactnetcrc_train
## -0.2851389 0.3824949mod.tar3$qr1$coefficients## intercept-sactnetcrc_train lag1-sactnetcrc_train
## 38543.7938885 0.6614203
## lag2-sactnetcrc_train lag3-sactnetcrc_train
## -0.7022195 0.9513138mod.tar1$qr2$coefficients## intercept-sactnetcrc_train lag1-sactnetcrc_train
## 81703.567757 0.897547mod.tar2$qr2$coefficients## intercept-sactnetcrc_train lag1-sactnetcrc_train
## 88446.1214206 0.8892529mod.tar3$qr2$coefficients## intercept-sactnetcrc_train lag1-sactnetcrc_train
## 34728.59159827 0.78818409
## lag2-sactnetcrc_train lag3-sactnetcrc_train
## -0.05905747 0.23115235cbind(
Modelo=c("1.TAR p1=3,p2=2,d=1",
"2.TAR p1=3,p2=1,d=2",
"3.TAR p1=3,p2=3,d=1"),
AIC=c(mod.tar1$AIC,
mod.tar2$AIC,
mod.tar3$AIC))%>%
knitr::kable()| Modelo | AIC | |
|---|---|---|
| 1 | 1.TAR p1=3,p2=2,d=1 | 2930 |
| 1 | 2.TAR p1=3,p2=1,d=2 | 2925 |
| 1 | 3.TAR p1=3,p2=3,d=1 | 2929 |
#tsdiag(mod.tar1)
tsdiag(mod.tar2)
#tsdiag(mod.tar3)
checkresiduals(ts(mod.tar1$residuals,start=inicio_train,frequency = 12))
checkresiduals(ts(mod.tar2$residuals,start=inicio_train,frequency = 12))
checkresiduals(ts(mod.tar3$residuals,start=inicio_train,frequency = 12))
prontar1<- ts(as.vector(predict(mod.tar1,n.ahead=h.param,n.sim=1000)$fit),start=inicio_test,frequency = 12)
prontar2<- ts(as.vector(predict(mod.tar2,n.ahead=h.param,n.sim=1000)$fit),start=inicio_test,frequency = 12)
prontar3<- ts(as.vector(predict(mod.tar3,n.ahead=h.param,n.sim=1000)$fit),start=inicio_test,frequency = 12)
fit1<-ts(as.vector(mod.tar1$y)-as.vector(mod.tar1$residuals),start =inicio_train,frequency = 12)
fit2<-ts(sactnetcrc_train-mod.tar2$residuals,start =inicio_train,frequency = 12)
fit3<-ts(sactnetcrc_train-mod.tar3$residuals,start =inicio_train,frequency = 12)MetricasTARCRC<-data.frame(
Modelo=rep(c("1.TAR p1=3,p2=2,d=1",
"2.TAR p1=3,p2=1,d=2",
"3.TAR p1=3,p2=3,d=1"),2),
DataSet= c(rep("Entrenamiento",3),rep("Prueba",3)),
rbind(getPerformance(fit1,sactnetcrc_train),
getPerformance(fit2,sactnetcrc_train),
getPerformance(fit3,sactnetcrc_train),
getPerformance(prontar1,sactnetcrc_test),
getPerformance(prontar2,sactnetcrc_test),
getPerformance(prontar3,sactnetcrc_test)))%>%
arrange(DataSet,RMSE)
MetricasTARCRC%>%
knitr::kable(caption="Metricas de Rendimiento Modelos TAR")| Modelo | DataSet | MAE | RSS | MSE | RMSE |
|---|---|---|---|---|---|
| 3.TAR p1=3,p2=3,d=1 | Entrenamiento | 39772.97 | 328942351055 | 2696248779 | 51925.42 |
| 1.TAR p1=3,p2=2,d=1 | Entrenamiento | 40308.12 | 338913405336 | 2777978732 | 52706.53 |
| 2.TAR p1=3,p2=1,d=2 | Entrenamiento | 40514.30 | 351547834112 | 2881539624 | 53679.97 |
| 3.TAR p1=3,p2=3,d=1 | Prueba | 59009.97 | 30898688005 | 6179737601 | 78611.31 |
| 1.TAR p1=3,p2=2,d=1 | Prueba | 84725.95 | 62293400375 | 12458680075 | 111618.46 |
| 2.TAR p1=3,p2=1,d=2 | Prueba | 87059.11 | 64393263703 | 12878652741 | 113484.15 |
autoplot(sactnetcrc_train)+
autolayer(fit1)+
autolayer(fit2)+
autolayer(fit3)+
theme_bw()
autoplot(sactnetcrc_test)+
autolayer(prontar1)+
autolayer(prontar2)+
autolayer(prontar3)+
theme_bw()+
scale_y_continuous(limits = c(500000,1400000))
SETAR
Thus the threshold delay, the number of lags in each regime and the threshold value are computed.
Setar1 <-
selectSETAR(
sactnetcrc_train,
include = c("const", "trend","none", "both"),
m = 3,
thDelay = seq(1, 2, by = 1),
nthresh = 2,
criterion = "AIC",
type = "level",
plot = T,
trace = T
)## Using maximum autoregressive order for low regime: mL = 3
## Using maximum autoregressive order for high regime: mH = 3
## Using maximum autoregressive order for middle regime: mM = 3
## Searching on 83 possible threshold values within regimes with sufficient ( 15% ) number of observations
## Searching on 1494 combinations of thresholds ( 83 ), thDelay ( 2 ), mL ( 3 ) and MM ( 3 )
##
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5Result of the one threshold search:
## -Thresh: 778351.5 -Delay: 2 -mL: 3 -mH: 1 - AIC 2664.942
##
## Trim not respected: 0.1680672 0.697479 0.1344538 from 416644.8 778351.5
## Trim not respected: 0.1764706 0.6890756 0.1344538 from 418187.9 778351.5
## Trim not respected: 0.1848739 0.6806723 0.1344538 from 422751.8 778351.5
## Trim not respected: 0.1932773 0.6722689 0.1344538 from 438446.4 778351.5
## Trim not respected: 0.210084 0.6554622 0.1344538 from 470793.3 778351.5
## Trim not respected: 0.2184874 0.6470588 0.1344538 from 492453.3 778351.5
## Trim not respected: 0.2268908 0.6386555 0.1344538 from 496086.7 778351.5
## Trim not respected: 0.2352941 0.6302521 0.1344538 from 499552.3 778351.5
## Trim not respected: 0.2436975 0.6218487 0.1344538 from 510928.7 778351.5
## Trim not respected: 0.2521008 0.6134454 0.1344538 from 511224.5 778351.5
## Trim not respected: 0.2605042 0.605042 0.1344538 from 511791.8 778351.5
## Trim not respected: 0.2689076 0.5966387 0.1344538 from 511895.9 778351.5
## Trim not respected: 0.2773109 0.5882353 0.1344538 from 512476.7 778351.5
## Trim not respected: 0.2857143 0.5798319 0.1344538 from 521049.7 778351.5
## Trim not respected: 0.2941176 0.5714286 0.1344538 from 523480.9 778351.5
## Trim not respected: 0.302521 0.5630252 0.1344538 from 528105.4 778351.5
## Trim not respected: 0.3109244 0.5546218 0.1344538 from 529852.4 778351.5
## Trim not respected: 0.3193277 0.5462185 0.1344538 from 530010.3 778351.5
## Trim not respected: 0.3277311 0.5378151 0.1344538 from 542167.5 778351.5
## Trim not respected: 0.3361345 0.5294118 0.1344538 from 544430 778351.5
## Trim not respected: 0.3445378 0.5210084 0.1344538 from 546553.9 778351.5
## Trim not respected: 0.3529412 0.512605 0.1344538 from 550118.6 778351.5
## Trim not respected: 0.3613445 0.5042017 0.1344538 from 557325.5 778351.5
## Trim not respected: 0.3697479 0.4957983 0.1344538 from 561437.7 778351.5
## Trim not respected: 0.3781513 0.487395 0.1344538 from 561724.3 778351.5
## Trim not respected: 0.3865546 0.4789916 0.1344538 from 562865.8 778351.5
## Trim not respected: 0.394958 0.4705882 0.1344538 from 581770.8 778351.5
## Trim not respected: 0.4033613 0.4621849 0.1344538 from 582916.3 778351.5
## Trim not respected: 0.4117647 0.4537815 0.1344538 from 596791 778351.5
## Trim not respected: 0.4201681 0.4453782 0.1344538 from 600365 778351.5
## Trim not respected: 0.4285714 0.4369748 0.1344538 from 615605.6 778351.5
## Trim not respected: 0.4369748 0.4285714 0.1344538 from 620337.8 778351.5
## Trim not respected: 0.4453782 0.4201681 0.1344538 from 621904.5 778351.5
## Trim not respected: 0.4537815 0.4117647 0.1344538 from 622250.1 778351.5
## Trim not respected: 0.4621849 0.4033613 0.1344538 from 629474.1 778351.5
## Trim not respected: 0.4705882 0.394958 0.1344538 from 634783.1 778351.5
## Trim not respected: 0.4789916 0.3865546 0.1344538 from 639380.1 778351.5
## Trim not respected: 0.487395 0.3781513 0.1344538 from 641543.7 778351.5
## Trim not respected: 0.4957983 0.3697479 0.1344538 from 643093.6 778351.5
## Trim not respected: 0.5042017 0.3613445 0.1344538 from 648906.5 778351.5
## Trim not respected: 0.512605 0.3529412 0.1344538 from 654757 778351.5
## Trim not respected: 0.5210084 0.3445378 0.1344538 from 666744 778351.5
## Trim not respected: 0.5294118 0.3361345 0.1344538 from 682393.1 778351.5
## Trim not respected: 0.5378151 0.3277311 0.1344538 from 685707.6 778351.5
## Trim not respected: 0.5462185 0.3193277 0.1344538 from 687379.9 778351.5
## Trim not respected: 0.5546218 0.3109244 0.1344538 from 687628.7 778351.5
## Trim not respected: 0.5630252 0.302521 0.1344538 from 689025.3 778351.5
## Trim not respected: 0.5714286 0.2941176 0.1344538 from 694178.9 778351.5
## Trim not respected: 0.5798319 0.2857143 0.1344538 from 695528 778351.5
## Trim not respected: 0.5882353 0.2773109 0.1344538 from 695643.6 778351.5
## Trim not respected: 0.5966387 0.2689076 0.1344538 from 699826.5 778351.5
## Trim not respected: 0.605042 0.2605042 0.1344538 from 701399.4 778351.5
## Trim not respected: 0.6134454 0.2521008 0.1344538 from 701843.8 778351.5
## Trim not respected: 0.6218487 0.2436975 0.1344538 from 702153.4 778351.5
## Trim not respected: 0.6302521 0.2352941 0.1344538 from 703858 778351.5
## Trim not respected: 0.6386555 0.2268908 0.1344538 from 705830 778351.5
## Trim not respected: 0.6470588 0.2184874 0.1344538 from 708095.7 778351.5
## Trim not respected: 0.6554622 0.210084 0.1344538 from 713654.1 778351.5
## Trim not respected: 0.6638655 0.2016807 0.1344538 from 717484.9 778351.5
## Trim not respected: 0.6722689 0.1932773 0.1344538 from 718458 778351.5
## Trim not respected: 0.6806723 0.1848739 0.1344538 from 721993.2 778351.5
## Trim not respected: 0.6890756 0.1764706 0.1344538 from 722997.2 778351.5
## Trim not respected: 0.697479 0.1680672 0.1344538 from 723361.2 778351.5
## Trim not respected: 0.7058824 0.1596639 0.1344538 from 725680.7 778351.5
## Trim not respected: 0.7142857 0.1512605 0.1344538 from 726081.6 778351.5Second best: 705830 (conditionnal on th= 778351.5 and Delay= 2 ) SSR/AIC: 2662.995
##
## Trim not respected: 0.6386555 0.2268908 0.1344538 from 705830 778351.5Second best: 778351.5 (conditionnal on th= 705830 and Delay= 2 ) SSR/AIC: 2662.995
##
## Trim not respected: 0.1680672 0.697479 0.1344538 from 416644.8 778351.5
## Trim not respected: 0.1764706 0.6890756 0.1344538 from 418187.9 778351.5
## Trim not respected: 0.1848739 0.6806723 0.1344538 from 422751.8 778351.5
## Trim not respected: 0.1932773 0.6722689 0.1344538 from 438446.4 778351.5
## Trim not respected: 0.210084 0.6554622 0.1344538 from 470793.3 778351.5
## Trim not respected: 0.2184874 0.6470588 0.1344538 from 492453.3 778351.5
## Trim not respected: 0.2268908 0.6386555 0.1344538 from 496086.7 778351.5
## Trim not respected: 0.2352941 0.6302521 0.1344538 from 499552.3 778351.5
## Trim not respected: 0.2436975 0.6218487 0.1344538 from 510928.7 778351.5
## Trim not respected: 0.2521008 0.6134454 0.1344538 from 511224.5 778351.5
## Trim not respected: 0.2605042 0.605042 0.1344538 from 511791.8 778351.5
## Trim not respected: 0.2689076 0.5966387 0.1344538 from 511895.9 778351.5
## Trim not respected: 0.2773109 0.5882353 0.1344538 from 512476.7 778351.5
## Trim not respected: 0.2857143 0.5798319 0.1344538 from 521049.7 778351.5
## Trim not respected: 0.2941176 0.5714286 0.1344538 from 523480.9 778351.5
## Trim not respected: 0.302521 0.5630252 0.1344538 from 528105.4 778351.5
## Trim not respected: 0.3109244 0.5546218 0.1344538 from 529852.4 778351.5
## Trim not respected: 0.3193277 0.5462185 0.1344538 from 530010.3 778351.5
## Trim not respected: 0.3277311 0.5378151 0.1344538 from 542167.5 778351.5
## Trim not respected: 0.3361345 0.5294118 0.1344538 from 544430 778351.5
## Trim not respected: 0.3445378 0.5210084 0.1344538 from 546553.9 778351.5
## Trim not respected: 0.3529412 0.512605 0.1344538 from 550118.6 778351.5
## Trim not respected: 0.3613445 0.5042017 0.1344538 from 557325.5 778351.5
## Trim not respected: 0.3697479 0.4957983 0.1344538 from 561437.7 778351.5
## Trim not respected: 0.3781513 0.487395 0.1344538 from 561724.3 778351.5
## Trim not respected: 0.3865546 0.4789916 0.1344538 from 562865.8 778351.5
## Trim not respected: 0.394958 0.4705882 0.1344538 from 581770.8 778351.5
## Trim not respected: 0.4033613 0.4621849 0.1344538 from 582916.3 778351.5
## Trim not respected: 0.4117647 0.4537815 0.1344538 from 596791 778351.5
## Trim not respected: 0.4201681 0.4453782 0.1344538 from 600365 778351.5
## Trim not respected: 0.4285714 0.4369748 0.1344538 from 615605.6 778351.5
## Trim not respected: 0.4369748 0.4285714 0.1344538 from 620337.8 778351.5
## Trim not respected: 0.4453782 0.4201681 0.1344538 from 621904.5 778351.5
## Trim not respected: 0.4537815 0.4117647 0.1344538 from 622250.1 778351.5
## Trim not respected: 0.4621849 0.4033613 0.1344538 from 629474.1 778351.5
## Trim not respected: 0.4705882 0.394958 0.1344538 from 634783.1 778351.5
## Trim not respected: 0.4789916 0.3865546 0.1344538 from 639380.1 778351.5
## Trim not respected: 0.487395 0.3781513 0.1344538 from 641543.7 778351.5
## Trim not respected: 0.4957983 0.3697479 0.1344538 from 643093.6 778351.5
## Trim not respected: 0.5042017 0.3613445 0.1344538 from 648906.5 778351.5
## Trim not respected: 0.512605 0.3529412 0.1344538 from 654757 778351.5
## Trim not respected: 0.5210084 0.3445378 0.1344538 from 666744 778351.5
## Trim not respected: 0.5294118 0.3361345 0.1344538 from 682393.1 778351.5
## Trim not respected: 0.5378151 0.3277311 0.1344538 from 685707.6 778351.5
## Trim not respected: 0.5462185 0.3193277 0.1344538 from 687379.9 778351.5
## Trim not respected: 0.5546218 0.3109244 0.1344538 from 687628.7 778351.5
## Trim not respected: 0.5630252 0.302521 0.1344538 from 689025.3 778351.5
## Trim not respected: 0.5714286 0.2941176 0.1344538 from 694178.9 778351.5
## Trim not respected: 0.5798319 0.2857143 0.1344538 from 695528 778351.5
## Trim not respected: 0.5882353 0.2773109 0.1344538 from 695643.6 778351.5
## Trim not respected: 0.5966387 0.2689076 0.1344538 from 699826.5 778351.5
## Trim not respected: 0.605042 0.2605042 0.1344538 from 701399.4 778351.5
## Trim not respected: 0.6134454 0.2521008 0.1344538 from 701843.8 778351.5
## Trim not respected: 0.6218487 0.2436975 0.1344538 from 702153.4 778351.5
## Trim not respected: 0.6302521 0.2352941 0.1344538 from 703858 778351.5
## Trim not respected: 0.6386555 0.2268908 0.1344538 from 705830 778351.5
## Trim not respected: 0.6470588 0.2184874 0.1344538 from 708095.7 778351.5
## Trim not respected: 0.6554622 0.210084 0.1344538 from 713654.1 778351.5
## Trim not respected: 0.6638655 0.2016807 0.1344538 from 717484.9 778351.5
## Trim not respected: 0.6722689 0.1932773 0.1344538 from 718458 778351.5
## Trim not respected: 0.6806723 0.1848739 0.1344538 from 721993.2 778351.5
## Trim not respected: 0.6890756 0.1764706 0.1344538 from 722997.2 778351.5
## Trim not respected: 0.697479 0.1680672 0.1344538 from 723361.2 778351.5
## Trim not respected: 0.7058824 0.1596639 0.1344538 from 725680.7 778351.5
## Trim not respected: 0.7142857 0.1512605 0.1344538 from 726081.6 778351.5Second best: 705830 (conditionnal on th= 778351.5 and Delay= 2 ) SSR/AIC: 2660.377
##
## Trim not respected: 0.6386555 0.2268908 0.1344538 from 705830 778351.5Second best: 771089.2 (conditionnal on th= 705830 and Delay= 2 ) SSR/AIC: 2660.162
##
## Trim not respected: 0.1680672 0.697479 0.1344538 from 416644.8 778351.5
## Trim not respected: 0.1764706 0.6890756 0.1344538 from 418187.9 778351.5
## Trim not respected: 0.1848739 0.6806723 0.1344538 from 422751.8 778351.5
## Trim not respected: 0.1932773 0.6722689 0.1344538 from 438446.4 778351.5
## Trim not respected: 0.210084 0.6554622 0.1344538 from 470793.3 778351.5
## Trim not respected: 0.2184874 0.6470588 0.1344538 from 492453.3 778351.5
## Trim not respected: 0.2268908 0.6386555 0.1344538 from 496086.7 778351.5
## Trim not respected: 0.2352941 0.6302521 0.1344538 from 499552.3 778351.5
## Trim not respected: 0.2436975 0.6218487 0.1344538 from 510928.7 778351.5
## Trim not respected: 0.2521008 0.6134454 0.1344538 from 511224.5 778351.5
## Trim not respected: 0.2605042 0.605042 0.1344538 from 511791.8 778351.5
## Trim not respected: 0.2689076 0.5966387 0.1344538 from 511895.9 778351.5
## Trim not respected: 0.2773109 0.5882353 0.1344538 from 512476.7 778351.5
## Trim not respected: 0.2857143 0.5798319 0.1344538 from 521049.7 778351.5
## Trim not respected: 0.2941176 0.5714286 0.1344538 from 523480.9 778351.5
## Trim not respected: 0.302521 0.5630252 0.1344538 from 528105.4 778351.5
## Trim not respected: 0.3109244 0.5546218 0.1344538 from 529852.4 778351.5
## Trim not respected: 0.3193277 0.5462185 0.1344538 from 530010.3 778351.5
## Trim not respected: 0.3277311 0.5378151 0.1344538 from 542167.5 778351.5
## Trim not respected: 0.3361345 0.5294118 0.1344538 from 544430 778351.5
## Trim not respected: 0.3445378 0.5210084 0.1344538 from 546553.9 778351.5
## Trim not respected: 0.3529412 0.512605 0.1344538 from 550118.6 778351.5
## Trim not respected: 0.3613445 0.5042017 0.1344538 from 557325.5 778351.5
## Trim not respected: 0.3697479 0.4957983 0.1344538 from 561437.7 778351.5
## Trim not respected: 0.3781513 0.487395 0.1344538 from 561724.3 778351.5
## Trim not respected: 0.3865546 0.4789916 0.1344538 from 562865.8 778351.5
## Trim not respected: 0.394958 0.4705882 0.1344538 from 581770.8 778351.5
## Trim not respected: 0.4033613 0.4621849 0.1344538 from 582916.3 778351.5
## Trim not respected: 0.4117647 0.4537815 0.1344538 from 596791 778351.5
## Trim not respected: 0.4201681 0.4453782 0.1344538 from 600365 778351.5
## Trim not respected: 0.4285714 0.4369748 0.1344538 from 615605.6 778351.5
## Trim not respected: 0.4369748 0.4285714 0.1344538 from 620337.8 778351.5
## Trim not respected: 0.4453782 0.4201681 0.1344538 from 621904.5 778351.5
## Trim not respected: 0.4537815 0.4117647 0.1344538 from 622250.1 778351.5
## Trim not respected: 0.4621849 0.4033613 0.1344538 from 629474.1 778351.5
## Trim not respected: 0.4705882 0.394958 0.1344538 from 634783.1 778351.5
## Trim not respected: 0.4789916 0.3865546 0.1344538 from 639380.1 778351.5
## Trim not respected: 0.487395 0.3781513 0.1344538 from 641543.7 778351.5
## Trim not respected: 0.4957983 0.3697479 0.1344538 from 643093.6 778351.5
## Trim not respected: 0.5042017 0.3613445 0.1344538 from 648906.5 778351.5
## Trim not respected: 0.512605 0.3529412 0.1344538 from 654757 778351.5
## Trim not respected: 0.5210084 0.3445378 0.1344538 from 666744 778351.5
## Trim not respected: 0.5294118 0.3361345 0.1344538 from 682393.1 778351.5
## Trim not respected: 0.5378151 0.3277311 0.1344538 from 685707.6 778351.5
## Trim not respected: 0.5462185 0.3193277 0.1344538 from 687379.9 778351.5
## Trim not respected: 0.5546218 0.3109244 0.1344538 from 687628.7 778351.5
## Trim not respected: 0.5630252 0.302521 0.1344538 from 689025.3 778351.5
## Trim not respected: 0.5714286 0.2941176 0.1344538 from 694178.9 778351.5
## Trim not respected: 0.5798319 0.2857143 0.1344538 from 695528 778351.5
## Trim not respected: 0.5882353 0.2773109 0.1344538 from 695643.6 778351.5
## Trim not respected: 0.5966387 0.2689076 0.1344538 from 699826.5 778351.5
## Trim not respected: 0.605042 0.2605042 0.1344538 from 701399.4 778351.5
## Trim not respected: 0.6134454 0.2521008 0.1344538 from 701843.8 778351.5
## Trim not respected: 0.6218487 0.2436975 0.1344538 from 702153.4 778351.5
## Trim not respected: 0.6302521 0.2352941 0.1344538 from 703858 778351.5
## Trim not respected: 0.6386555 0.2268908 0.1344538 from 705830 778351.5
## Trim not respected: 0.6470588 0.2184874 0.1344538 from 708095.7 778351.5
## Trim not respected: 0.6554622 0.210084 0.1344538 from 713654.1 778351.5
## Trim not respected: 0.6638655 0.2016807 0.1344538 from 717484.9 778351.5
## Trim not respected: 0.6722689 0.1932773 0.1344538 from 718458 778351.5
## Trim not respected: 0.6806723 0.1848739 0.1344538 from 721993.2 778351.5
## Trim not respected: 0.6890756 0.1764706 0.1344538 from 722997.2 778351.5
## Trim not respected: 0.697479 0.1680672 0.1344538 from 723361.2 778351.5
## Trim not respected: 0.7058824 0.1596639 0.1344538 from 725680.7 778351.5
## Trim not respected: 0.7142857 0.1512605 0.1344538 from 726081.6 778351.5Second best: 705830 (conditionnal on th= 778351.5 and Delay= 2 ) SSR/AIC: 2662.311
##
## Trim not respected: 0.6386555 0.2268908 0.1344538 from 705830 778351.5Second best: 771089.2 (conditionnal on th= 705830 and Delay= 2 ) SSR/AIC: 2661.953
Setar2 <-
selectSETAR(
sactnetcrc_train,
m = 3,
d=2,
thDelay = seq(1, 2, by = 1),
nthresh = 2,
criterion = "AIC",
type = "level",
plot = T,
trace = T
)## Using maximum autoregressive order for low regime: mL = 3
## Using maximum autoregressive order for high regime: mH = 3
## Using maximum autoregressive order for middle regime: mM = 3
## Searching on 80 possible threshold values within regimes with sufficient ( 15% ) number of observations
## Searching on 1440 combinations of thresholds ( 80 ), thDelay ( 2 ), mL ( 3 ) and MM ( 3 )
##
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2Result of the one threshold search:
## -Thresh: 771089.2 -Delay: 2 -mL: 2 -mH: 1 - AIC 2714.372
##
## Trim not respected: 0.1896552 0.6896552 0.1206897 from 422751.8 771089.2
## Trim not respected: 0.1982759 0.6810345 0.1206897 from 438446.4 771089.2
## Trim not respected: 0.2155172 0.6637931 0.1206897 from 470793.3 771089.2
## Trim not respected: 0.2241379 0.6551724 0.1206897 from 492453.3 771089.2
## Trim not respected: 0.2327586 0.6465517 0.1206897 from 496086.7 771089.2
## Trim not respected: 0.2413793 0.637931 0.1206897 from 499552.3 771089.2
## Trim not respected: 0.25 0.6293103 0.1206897 from 510928.7 771089.2
## Trim not respected: 0.2586207 0.6206897 0.1206897 from 511224.5 771089.2
## Trim not respected: 0.2672414 0.612069 0.1206897 from 511791.8 771089.2
## Trim not respected: 0.2758621 0.6034483 0.1206897 from 511895.9 771089.2
## Trim not respected: 0.2844828 0.5948276 0.1206897 from 512476.7 771089.2
## Trim not respected: 0.2931034 0.5862069 0.1206897 from 521049.7 771089.2
## Trim not respected: 0.3017241 0.5775862 0.1206897 from 523480.9 771089.2
## Trim not respected: 0.3103448 0.5689655 0.1206897 from 528105.4 771089.2
## Trim not respected: 0.3189655 0.5603448 0.1206897 from 529852.4 771089.2
## Trim not respected: 0.3275862 0.5517241 0.1206897 from 530010.3 771089.2
## Trim not respected: 0.3362069 0.5431034 0.1206897 from 542167.5 771089.2
## Trim not respected: 0.3448276 0.5344828 0.1206897 from 544430 771089.2
## Trim not respected: 0.3534483 0.5258621 0.1206897 from 546553.9 771089.2
## Trim not respected: 0.362069 0.5172414 0.1206897 from 550118.6 771089.2
## Trim not respected: 0.3706897 0.5086207 0.1206897 from 557325.5 771089.2
## Trim not respected: 0.3793103 0.5 0.1206897 from 561437.7 771089.2
## Trim not respected: 0.387931 0.4913793 0.1206897 from 561724.3 771089.2
## Trim not respected: 0.3965517 0.4827586 0.1206897 from 562865.8 771089.2
## Trim not respected: 0.4051724 0.4741379 0.1206897 from 581770.8 771089.2
## Trim not respected: 0.4137931 0.4655172 0.1206897 from 582916.3 771089.2
## Trim not respected: 0.4224138 0.4568966 0.1206897 from 596791 771089.2
## Trim not respected: 0.4310345 0.4482759 0.1206897 from 600365 771089.2
## Trim not respected: 0.4396552 0.4396552 0.1206897 from 615605.6 771089.2
## Trim not respected: 0.4482759 0.4310345 0.1206897 from 620337.8 771089.2
## Trim not respected: 0.4568966 0.4224138 0.1206897 from 621904.5 771089.2
## Trim not respected: 0.4655172 0.4137931 0.1206897 from 622250.1 771089.2
## Trim not respected: 0.4741379 0.4051724 0.1206897 from 629474.1 771089.2
## Trim not respected: 0.4827586 0.3965517 0.1206897 from 634783.1 771089.2
## Trim not respected: 0.4913793 0.387931 0.1206897 from 639380.1 771089.2
## Trim not respected: 0.5 0.3793103 0.1206897 from 641543.7 771089.2
## Trim not respected: 0.5086207 0.3706897 0.1206897 from 643093.6 771089.2
## Trim not respected: 0.5172414 0.362069 0.1206897 from 648906.5 771089.2
## Trim not respected: 0.5258621 0.3534483 0.1206897 from 654757 771089.2
## Trim not respected: 0.5344828 0.3448276 0.1206897 from 666744 771089.2
## Trim not respected: 0.5431034 0.3362069 0.1206897 from 682393.1 771089.2
## Trim not respected: 0.5517241 0.3275862 0.1206897 from 685707.6 771089.2
## Trim not respected: 0.5603448 0.3189655 0.1206897 from 687379.9 771089.2
## Trim not respected: 0.5689655 0.3103448 0.1206897 from 687628.7 771089.2
## Trim not respected: 0.5775862 0.3017241 0.1206897 from 689025.3 771089.2
## Trim not respected: 0.5862069 0.2931034 0.1206897 from 694178.9 771089.2
## Trim not respected: 0.5948276 0.2844828 0.1206897 from 695528 771089.2
## Trim not respected: 0.6034483 0.2758621 0.1206897 from 695643.6 771089.2
## Trim not respected: 0.612069 0.2672414 0.1206897 from 699826.5 771089.2
## Trim not respected: 0.6206897 0.2586207 0.1206897 from 701399.4 771089.2
## Trim not respected: 0.6293103 0.25 0.1206897 from 701843.8 771089.2
## Trim not respected: 0.637931 0.2413793 0.1206897 from 702153.4 771089.2
## Trim not respected: 0.6465517 0.2327586 0.1206897 from 703858 771089.2
## Trim not respected: 0.6551724 0.2241379 0.1206897 from 705830 771089.2
## Trim not respected: 0.6637931 0.2155172 0.1206897 from 708095.7 771089.2
## Trim not respected: 0.6724138 0.2068966 0.1206897 from 713654.1 771089.2
## Trim not respected: 0.6810345 0.1982759 0.1206897 from 717484.9 771089.2
## Trim not respected: 0.6896552 0.1896552 0.1206897 from 718458 771089.2
## Trim not respected: 0.6982759 0.1810345 0.1206897 from 721993.2 771089.2
## Trim not respected: 0.7068966 0.1724138 0.1206897 from 722997.2 771089.2
## Trim not respected: 0.7155172 0.1637931 0.1206897 from 723361.2 771089.2
## Trim not respected: 0.7241379 0.1551724 0.1206897 from 725680.7 771089.2Second best: 725680.7 (conditionnal on th= 771089.2 and Delay= 2 ) SSR/AIC: 2715.108
## Second best: 529852.4 (conditionnal on th= 725680.7 and Delay= 2 ) SSR/AIC: 2732.59
##
## Trim not respected: 0.1896552 0.6896552 0.1206897 from 422751.8 771089.2
## Trim not respected: 0.1982759 0.6810345 0.1206897 from 438446.4 771089.2
## Trim not respected: 0.2155172 0.6637931 0.1206897 from 470793.3 771089.2
## Trim not respected: 0.2241379 0.6551724 0.1206897 from 492453.3 771089.2
## Trim not respected: 0.2327586 0.6465517 0.1206897 from 496086.7 771089.2
## Trim not respected: 0.2413793 0.637931 0.1206897 from 499552.3 771089.2
## Trim not respected: 0.25 0.6293103 0.1206897 from 510928.7 771089.2
## Trim not respected: 0.2586207 0.6206897 0.1206897 from 511224.5 771089.2
## Trim not respected: 0.2672414 0.612069 0.1206897 from 511791.8 771089.2
## Trim not respected: 0.2758621 0.6034483 0.1206897 from 511895.9 771089.2
## Trim not respected: 0.2844828 0.5948276 0.1206897 from 512476.7 771089.2
## Trim not respected: 0.2931034 0.5862069 0.1206897 from 521049.7 771089.2
## Trim not respected: 0.3017241 0.5775862 0.1206897 from 523480.9 771089.2
## Trim not respected: 0.3103448 0.5689655 0.1206897 from 528105.4 771089.2
## Trim not respected: 0.3189655 0.5603448 0.1206897 from 529852.4 771089.2
## Trim not respected: 0.3275862 0.5517241 0.1206897 from 530010.3 771089.2
## Trim not respected: 0.3362069 0.5431034 0.1206897 from 542167.5 771089.2
## Trim not respected: 0.3448276 0.5344828 0.1206897 from 544430 771089.2
## Trim not respected: 0.3534483 0.5258621 0.1206897 from 546553.9 771089.2
## Trim not respected: 0.362069 0.5172414 0.1206897 from 550118.6 771089.2
## Trim not respected: 0.3706897 0.5086207 0.1206897 from 557325.5 771089.2
## Trim not respected: 0.3793103 0.5 0.1206897 from 561437.7 771089.2
## Trim not respected: 0.387931 0.4913793 0.1206897 from 561724.3 771089.2
## Trim not respected: 0.3965517 0.4827586 0.1206897 from 562865.8 771089.2
## Trim not respected: 0.4051724 0.4741379 0.1206897 from 581770.8 771089.2
## Trim not respected: 0.4137931 0.4655172 0.1206897 from 582916.3 771089.2
## Trim not respected: 0.4224138 0.4568966 0.1206897 from 596791 771089.2
## Trim not respected: 0.4310345 0.4482759 0.1206897 from 600365 771089.2
## Trim not respected: 0.4396552 0.4396552 0.1206897 from 615605.6 771089.2
## Trim not respected: 0.4482759 0.4310345 0.1206897 from 620337.8 771089.2
## Trim not respected: 0.4568966 0.4224138 0.1206897 from 621904.5 771089.2
## Trim not respected: 0.4655172 0.4137931 0.1206897 from 622250.1 771089.2
## Trim not respected: 0.4741379 0.4051724 0.1206897 from 629474.1 771089.2
## Trim not respected: 0.4827586 0.3965517 0.1206897 from 634783.1 771089.2
## Trim not respected: 0.4913793 0.387931 0.1206897 from 639380.1 771089.2
## Trim not respected: 0.5 0.3793103 0.1206897 from 641543.7 771089.2
## Trim not respected: 0.5086207 0.3706897 0.1206897 from 643093.6 771089.2
## Trim not respected: 0.5172414 0.362069 0.1206897 from 648906.5 771089.2
## Trim not respected: 0.5258621 0.3534483 0.1206897 from 654757 771089.2
## Trim not respected: 0.5344828 0.3448276 0.1206897 from 666744 771089.2
## Trim not respected: 0.5431034 0.3362069 0.1206897 from 682393.1 771089.2
## Trim not respected: 0.5517241 0.3275862 0.1206897 from 685707.6 771089.2
## Trim not respected: 0.5603448 0.3189655 0.1206897 from 687379.9 771089.2
## Trim not respected: 0.5689655 0.3103448 0.1206897 from 687628.7 771089.2
## Trim not respected: 0.5775862 0.3017241 0.1206897 from 689025.3 771089.2
## Trim not respected: 0.5862069 0.2931034 0.1206897 from 694178.9 771089.2
## Trim not respected: 0.5948276 0.2844828 0.1206897 from 695528 771089.2
## Trim not respected: 0.6034483 0.2758621 0.1206897 from 695643.6 771089.2
## Trim not respected: 0.612069 0.2672414 0.1206897 from 699826.5 771089.2
## Trim not respected: 0.6206897 0.2586207 0.1206897 from 701399.4 771089.2
## Trim not respected: 0.6293103 0.25 0.1206897 from 701843.8 771089.2
## Trim not respected: 0.637931 0.2413793 0.1206897 from 702153.4 771089.2
## Trim not respected: 0.6465517 0.2327586 0.1206897 from 703858 771089.2
## Trim not respected: 0.6551724 0.2241379 0.1206897 from 705830 771089.2
## Trim not respected: 0.6637931 0.2155172 0.1206897 from 708095.7 771089.2
## Trim not respected: 0.6724138 0.2068966 0.1206897 from 713654.1 771089.2
## Trim not respected: 0.6810345 0.1982759 0.1206897 from 717484.9 771089.2
## Trim not respected: 0.6896552 0.1896552 0.1206897 from 718458 771089.2
## Trim not respected: 0.6982759 0.1810345 0.1206897 from 721993.2 771089.2
## Trim not respected: 0.7068966 0.1724138 0.1206897 from 722997.2 771089.2
## Trim not respected: 0.7155172 0.1637931 0.1206897 from 723361.2 771089.2
## Trim not respected: 0.7241379 0.1551724 0.1206897 from 725680.7 771089.2Second best: 725680.7 (conditionnal on th= 771089.2 and Delay= 2 ) SSR/AIC: 2715.69
## Second best: 521049.7 (conditionnal on th= 725680.7 and Delay= 2 ) SSR/AIC: 2730.675
##
## Trim not respected: 0.1896552 0.6896552 0.1206897 from 422751.8 771089.2
## Trim not respected: 0.1982759 0.6810345 0.1206897 from 438446.4 771089.2
## Trim not respected: 0.2155172 0.6637931 0.1206897 from 470793.3 771089.2
## Trim not respected: 0.2241379 0.6551724 0.1206897 from 492453.3 771089.2
## Trim not respected: 0.2327586 0.6465517 0.1206897 from 496086.7 771089.2
## Trim not respected: 0.2413793 0.637931 0.1206897 from 499552.3 771089.2
## Trim not respected: 0.25 0.6293103 0.1206897 from 510928.7 771089.2
## Trim not respected: 0.2586207 0.6206897 0.1206897 from 511224.5 771089.2
## Trim not respected: 0.2672414 0.612069 0.1206897 from 511791.8 771089.2
## Trim not respected: 0.2758621 0.6034483 0.1206897 from 511895.9 771089.2
## Trim not respected: 0.2844828 0.5948276 0.1206897 from 512476.7 771089.2
## Trim not respected: 0.2931034 0.5862069 0.1206897 from 521049.7 771089.2
## Trim not respected: 0.3017241 0.5775862 0.1206897 from 523480.9 771089.2
## Trim not respected: 0.3103448 0.5689655 0.1206897 from 528105.4 771089.2
## Trim not respected: 0.3189655 0.5603448 0.1206897 from 529852.4 771089.2
## Trim not respected: 0.3275862 0.5517241 0.1206897 from 530010.3 771089.2
## Trim not respected: 0.3362069 0.5431034 0.1206897 from 542167.5 771089.2
## Trim not respected: 0.3448276 0.5344828 0.1206897 from 544430 771089.2
## Trim not respected: 0.3534483 0.5258621 0.1206897 from 546553.9 771089.2
## Trim not respected: 0.362069 0.5172414 0.1206897 from 550118.6 771089.2
## Trim not respected: 0.3706897 0.5086207 0.1206897 from 557325.5 771089.2
## Trim not respected: 0.3793103 0.5 0.1206897 from 561437.7 771089.2
## Trim not respected: 0.387931 0.4913793 0.1206897 from 561724.3 771089.2
## Trim not respected: 0.3965517 0.4827586 0.1206897 from 562865.8 771089.2
## Trim not respected: 0.4051724 0.4741379 0.1206897 from 581770.8 771089.2
## Trim not respected: 0.4137931 0.4655172 0.1206897 from 582916.3 771089.2
## Trim not respected: 0.4224138 0.4568966 0.1206897 from 596791 771089.2
## Trim not respected: 0.4310345 0.4482759 0.1206897 from 600365 771089.2
## Trim not respected: 0.4396552 0.4396552 0.1206897 from 615605.6 771089.2
## Trim not respected: 0.4482759 0.4310345 0.1206897 from 620337.8 771089.2
## Trim not respected: 0.4568966 0.4224138 0.1206897 from 621904.5 771089.2
## Trim not respected: 0.4655172 0.4137931 0.1206897 from 622250.1 771089.2
## Trim not respected: 0.4741379 0.4051724 0.1206897 from 629474.1 771089.2
## Trim not respected: 0.4827586 0.3965517 0.1206897 from 634783.1 771089.2
## Trim not respected: 0.4913793 0.387931 0.1206897 from 639380.1 771089.2
## Trim not respected: 0.5 0.3793103 0.1206897 from 641543.7 771089.2
## Trim not respected: 0.5086207 0.3706897 0.1206897 from 643093.6 771089.2
## Trim not respected: 0.5172414 0.362069 0.1206897 from 648906.5 771089.2
## Trim not respected: 0.5258621 0.3534483 0.1206897 from 654757 771089.2
## Trim not respected: 0.5344828 0.3448276 0.1206897 from 666744 771089.2
## Trim not respected: 0.5431034 0.3362069 0.1206897 from 682393.1 771089.2
## Trim not respected: 0.5517241 0.3275862 0.1206897 from 685707.6 771089.2
## Trim not respected: 0.5603448 0.3189655 0.1206897 from 687379.9 771089.2
## Trim not respected: 0.5689655 0.3103448 0.1206897 from 687628.7 771089.2
## Trim not respected: 0.5775862 0.3017241 0.1206897 from 689025.3 771089.2
## Trim not respected: 0.5862069 0.2931034 0.1206897 from 694178.9 771089.2
## Trim not respected: 0.5948276 0.2844828 0.1206897 from 695528 771089.2
## Trim not respected: 0.6034483 0.2758621 0.1206897 from 695643.6 771089.2
## Trim not respected: 0.612069 0.2672414 0.1206897 from 699826.5 771089.2
## Trim not respected: 0.6206897 0.2586207 0.1206897 from 701399.4 771089.2
## Trim not respected: 0.6293103 0.25 0.1206897 from 701843.8 771089.2
## Trim not respected: 0.637931 0.2413793 0.1206897 from 702153.4 771089.2
## Trim not respected: 0.6465517 0.2327586 0.1206897 from 703858 771089.2
## Trim not respected: 0.6551724 0.2241379 0.1206897 from 705830 771089.2
## Trim not respected: 0.6637931 0.2155172 0.1206897 from 708095.7 771089.2
## Trim not respected: 0.6724138 0.2068966 0.1206897 from 713654.1 771089.2
## Trim not respected: 0.6810345 0.1982759 0.1206897 from 717484.9 771089.2
## Trim not respected: 0.6896552 0.1896552 0.1206897 from 718458 771089.2
## Trim not respected: 0.6982759 0.1810345 0.1206897 from 721993.2 771089.2
## Trim not respected: 0.7068966 0.1724138 0.1206897 from 722997.2 771089.2
## Trim not respected: 0.7155172 0.1637931 0.1206897 from 723361.2 771089.2
## Trim not respected: 0.7241379 0.1551724 0.1206897 from 725680.7 771089.2Second best: 725680.7 (conditionnal on th= 771089.2 and Delay= 2 ) SSR/AIC: 2717.616
## Second best: 521049.7 (conditionnal on th= 725680.7 and Delay= 2 ) SSR/AIC: 2731.29
Setar3 <-
selectSETAR(
sactnetcrc_train,
m = 3,
thDelay = seq(0, 2, by = 1),
nthresh = 1,
d = 1,
criterion = "AIC",
type = "level",
plot = T,
trace = T
)## Using maximum autoregressive order for low regime: mL = 3
## Using maximum autoregressive order for high regime: mH = 3
## Searching on 83 possible threshold values within regimes with sufficient ( 15% ) number of observations
## Searching on 2241 combinations of thresholds ( 83 ), thDelay ( 3 ), mL ( 3 ) and MM ( 3 )
##
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8571429 0.1428571 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
Setar4 <-
selectSETAR(
sactnetcrc_train,
m = 3,
thDelay = seq(0, 2, by = 1),
nthresh = 1,
d = 2,
criterion = "AIC",
type = "level",
plot = T,
trace = T
)## Using maximum autoregressive order for low regime: mL = 3
## Using maximum autoregressive order for high regime: mH = 3
## Searching on 80 possible threshold values within regimes with sufficient ( 15% ) number of observations
## Searching on 2160 combinations of thresholds ( 80 ), thDelay ( 3 ), mL ( 3 ) and MM ( 3 )
##
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.862069 0.137931 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
Setar1$allTh%>%
as.data.frame()%>%
arrange(AIC,thDelay,mL,mH)%>%
head(5)## thDelay mL mH th AIC
## 1 2 3 1 778351.5 2664.942
## 2 2 3 1 730317.6 2664.952
## 3 2 3 1 733818.8 2665.123
## 4 2 3 1 748435.4 2665.137
## 5 2 3 1 748409.3 2665.149Setar2$allTh%>%
as.data.frame()%>%
arrange(AIC,thDelay,mL,mH)%>%
head(5)## thDelay mL mH th AIC
## 1 2 2 1 771089.2 2714.372
## 2 2 2 3 771089.2 2715.404
## 3 2 2 2 771089.2 2715.629
## 4 2 3 1 771089.2 2716.262
## 5 2 3 3 771089.2 2717.291Setar3$allTh%>%
as.data.frame()%>%
arrange(AIC,thDelay,mL,mH)%>%
head(5)## thDelay mL mH th AIC
## 1 2 3 1 778351.5 2664.942
## 2 2 3 1 730317.6 2664.952
## 3 2 3 1 733818.8 2665.123
## 4 2 3 1 748435.4 2665.137
## 5 2 3 1 748409.3 2665.149Setar4$allTh%>%
as.data.frame()%>%
arrange(AIC,thDelay,mL,mH)%>%
head(5)## thDelay mL mH th AIC
## 1 2 2 1 771089.2 2714.372
## 2 2 2 3 771089.2 2715.404
## 3 2 2 2 771089.2 2715.629
## 4 0 2 1 765999.6 2715.832
## 5 2 3 1 771089.2 2716.262modeloas1 <-
setar(
sactnetcrc_train,
m = 3,
mL = 3,
mH = 1,
d=1,
nthresh = 1,
thDelay = 2,
type = "level"
)##
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 778351.5## Raiz Unitaria
summary(modeloas1) #residuals variance = 0.005525, AIC = -632, MAPE = 0.4352%##
## Non linear autoregressive model
##
## SETAR model ( 2 regimes)
## Coefficients:
## Low regime:
## const.L phiL.1 phiL.2 phiL.3
## 15609.1159299 0.8269447 -0.2758435 0.4374676
##
## High regime:
## const.H phiH.1
## 115310.2619648 0.8862372
##
## Threshold:
## -Variable: Z(t) = + (0) X(t)+ (0)X(t-1)+ (1)X(t-2)
## -Value: 778351
## Proportion of points in low regime: 86.55% High regime: 13.45%
##
## Residuals:
## Min 1Q Median 3Q Max
## -154452.7 -35472.8 -2474.2 30816.0 172187.3
##
## Fit:
## residuals variance = 2734001726, AIC = 2665, MAPE = 6.472%
##
## Coefficient(s):
##
## Estimate Std. Error t value Pr(>|t|)
## const.L 15609.115930 23870.032713 0.6539 0.5144570
## phiL.1 0.826945 0.107621 7.6838 0.000000000005423 ***
## phiL.2 -0.275843 0.142709 -1.9329 0.0556856 .
## phiL.3 0.437468 0.113051 3.8696 0.0001805 ***
## const.H 115310.261965 68150.181595 1.6920 0.0933301 .
## phiH.1 0.886237 0.075633 11.7175 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold
## Variable: Z(t) = + (0) X(t) + (0) X(t-1)+ (1) X(t-2)
##
## Value: 778351# plot(modeloas1)
checkresiduals(ts(modeloas1$residuals,start=inicio_train,frequency = 12))
modeloas2 <-
setar(
sactnetcrc_train,
m = 3,
mL = 2,
mH = 3,
d=2,
nthresh = 1,
thDelay = 2,
type = "level"
)##
## 1 T: Trim not respected: 0.8534483 0.1465517 from th: 766323.9
## 1 T: Trim not respected: 0.862069 0.137931 from th: 768336.6
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2
## 1 T: Trim not respected: 0.8793103 0.1206897 from th: 771089.2## Raiz Unitaria
summary(modeloas2) # residuals variance = 0.005857, AIC = -635, MAPE = 0.4584%##
## Non linear autoregressive model
##
## SETAR model ( 2 regimes)
## Coefficients:
## Low regime:
## const.L phiL.1 phiL.2
## 34071.1080807 0.4627114 0.5021382
##
## High regime:
## const.H phiH.1 phiH.2 phiH.3
## -77861.2418850 1.0319769 -0.4420645 0.5041543
##
## Threshold:
## -Variable: Z(t) = + (0) X(t)+ (0)X(t-1)+ (1)X(t-2)
## -Value: 771089
## Proportion of points in low regime: 87.93% High regime: 12.07%
##
## Residuals:
## Min 1Q Median 3Q Max
## -142122.0 -39133.9 -6568.6 40446.3 200204.4
##
## Fit:
## residuals variance = 4067367517, AIC = 2715, MAPE = 7.964%
##
## Coefficient(s):
##
## Estimate Std. Error t value Pr(>|t|)
## const.L 34071.108081 28347.172029 1.2019 0.2319
## phiL.1 0.462711 0.091475 5.0583 0.0000016165 ***
## phiL.2 0.502138 0.092180 5.4474 0.0000002944 ***
## const.H -77861.241885 197462.308180 -0.3943 0.6941
## phiH.1 1.031977 0.220265 4.6852 0.0000077304 ***
## phiH.2 -0.442065 0.273944 -1.6137 0.1093
## phiH.3 0.504154 0.346506 1.4550 0.1484
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold
## Variable: Z(t) = + (0) X(t) + (0) X(t-1)+ (1) X(t-2)
##
## Value: 771089# plot(modeloas2)
checkresiduals(ts(modeloas2$residuals,start=inicio_train,frequency = 12))
modeloas3 <-
setar(
sactnetcrc_train,
m = 3,
mL = 3,
mH = 2,
d=1,
nthresh = 1,
thDelay = 0,
type = "level"
)
## Raiz Unitaria
summary(modeloas3) # residuals variance = 0.006319, AIC = -621, MAPE = 0.4621%##
## Non linear autoregressive model
##
## SETAR model ( 2 regimes)
## Coefficients:
## Low regime:
## const.L phiL.1 phiL.2 phiL.3
## -6554.1376992 0.7843393 -0.1477890 0.3951947
##
## High regime:
## const.H phiH.1 phiH.2
## -21230.4436532 1.1909899 -0.1927039
##
## Threshold:
## -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (0)X(t-2)
## -Value: 737895
## Proportion of points in low regime: 73.11% High regime: 26.89%
##
## Residuals:
## Min 1Q Median 3Q Max
## -212069.7 -26359.4 -2570.8 30755.0 159863.0
##
## Fit:
## residuals variance = 2741163981, AIC = 2667, MAPE = 6.291%
##
## Coefficient(s):
##
## Estimate Std. Error t value Pr(>|t|)
## const.L -6554.13770 27001.52675 -0.2427 0.808645
## phiL.1 0.78434 0.11867 6.6093 0.000000001262 ***
## phiL.2 -0.14779 0.15100 -0.9788 0.329752
## phiL.3 0.39519 0.12353 3.1993 0.001781 **
## const.H -21230.44365 67109.96245 -0.3164 0.752308
## phiH.1 1.19099 0.18970 6.2782 0.000000006271 ***
## phiH.2 -0.19270 0.17324 -1.1123 0.268311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold
## Variable: Z(t) = + (1) X(t) + (0) X(t-1)+ (0) X(t-2)
##
## Value: 737895# plot(modeloas3)
checkresiduals(ts(modeloas3$residuals,start=inicio_train,frequency = 12))
modeloas4 <-
setar(
sactnetcrc_train,
m = 3,
mL = 1,
mH = 2,
d=2,
nthresh = 1,
thDelay = 0,
type = "level"
)
summary(modeloas4) # residuals variance = 0.006319, AIC = -621, MAPE = 0.4621%##
## Non linear autoregressive model
##
## SETAR model ( 2 regimes)
## Coefficients:
## Low regime:
## const.L phiL.1
## 497047.101814 -0.245272
##
## High regime:
## const.H phiH.1 phiH.2
## 55962.5321562 0.6042356 0.3360949
##
## Threshold:
## -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (0)X(t-2)
## -Value: 438446
## Proportion of points in low regime: 17.24% High regime: 82.76%
##
## Residuals:
## Min 1Q Median 3Q Max
## -155836.0 -37647.5 -5849.8 34675.6 193708.0
##
## Fit:
## residuals variance = 4576252319, AIC = 2726, MAPE = 7.827%
##
## Coefficient(s):
##
## Estimate Std. Error t value Pr(>|t|)
## const.L 497047.101814 229225.967759 2.1684 0.0321548 *
## phiL.1 -0.245272 0.588794 -0.4166 0.6777585
## const.H 55962.532156 36809.220638 1.5203 0.1311234
## phiH.1 0.604236 0.090559 6.6723 0.0000000008819 ***
## phiH.2 0.336095 0.091306 3.6810 0.0003529 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold
## Variable: Z(t) = + (1) X(t) + (0) X(t-1)+ (0) X(t-2)
##
## Value: 438446# plot(modeloas4)
checkresiduals(ts(modeloas4$residuals,start=inicio_train,frequency = 12))
cbind(
Modelo=c("1.SETAR m = 3,mL = 3, mH = 1, d=1,nthresh = 1,thDelay = 2",
"2.SETAR m = 3,mL = 2, mH = 3, d=2,nthresh = 1,thDelay = 2",
"3.SETAR m = 3,mL = 3, mH = 2, d=1,nthresh = 1,thDelay = 0",
"4.SETAR m = 3,mL = 1, mH = 2, d=2,nthresh = 1,thDelay = 0"),
AIC=c(
AIC(modeloas1),
AIC(modeloas2),
AIC(modeloas3),
AIC(modeloas4)
),
BIC=c(
BIC(modeloas1),
BIC(modeloas2),
BIC(modeloas3),
BIC(modeloas4)
)
)%>%
knitr::kable()| Modelo | AIC | BIC |
|---|---|---|
| 1.SETAR m = 3,mL = 3, mH = 1, d=1,nthresh = 1,thDelay = 2 | 2664.94192921197 | 2684.57007652511 |
| 2.SETAR m = 3,mL = 2, mH = 3, d=2,nthresh = 1,thDelay = 2 | 2715.40394269942 | 2737.83611105729 |
| 3.SETAR m = 3,mL = 3, mH = 2, d=1,nthresh = 1,thDelay = 0 | 2667.26111427235 | 2689.69328263021 |
| 4.SETAR m = 3,mL = 1, mH = 2, d=2,nthresh = 1,thDelay = 0 | 2725.78583996026 | 2742.60996622866 |
pronsetar1<- predict(modeloas1, n.ahead = h.param)
pronsetar2<- predict(modeloas2, n.ahead = h.param)
pronsetar3<- predict(modeloas3, n.ahead = h.param)
pronsetar4<- predict(modeloas4, n.ahead = h.param)
fit1<-ts(modeloas1$fitted.values,start =inicio_train,frequency = 12)
fit2<-ts(modeloas2$fitted.values,start =inicio_train,frequency = 12)
fit3<-ts(modeloas3$fitted.values,start =inicio_train,frequency = 12)
fit4<-ts(modeloas4$fitted.values,start =inicio_train,frequency = 12)MetricasSETARCRC<-data.frame(
Modelo=rep(c(
"1.SETAR m = 3,mL = 3, mH = 1, d=1,nthresh = 1,thDelay = 2",
"2.SETAR m = 3,mL = 2, mH = 3, d=2,nthresh = 1,thDelay = 2",
"3.SETAR m = 3,mL = 3, mH = 2, d=1,nthresh = 1,thDelay = 0",
"4.SETAR m = 3,mL = 1, mH = 2, d=2,nthresh = 1,thDelay = 0"),2),
DataSet= c(rep("Entrenamiento",4),rep("Prueba",4)),
rbind(
getPerformance(fit1,sactnetcrc_train),
getPerformance(fit2,sactnetcrc_train),
getPerformance(fit3,sactnetcrc_train),
getPerformance(fit4,sactnetcrc_train),
getPerformance(pronsetar1,sactnetcrc_test),
getPerformance(pronsetar2,sactnetcrc_test),
getPerformance(pronsetar3,sactnetcrc_test),
getPerformance(pronsetar4,sactnetcrc_test)
))%>%
arrange(DataSet,RMSE)
MetricasSETARCRC%>%
knitr::kable(caption="Metricas de Rendimiento Modelos SETAR")| Modelo | DataSet | MAE | RSS | MSE | RMSE |
|---|---|---|---|---|---|
| 1.SETAR m = 3,mL = 3, mH = 1, d=1,nthresh = 1,thDelay = 2 | Entrenamiento | 42575.25 | 411939644750 | 3376554465 | 58108.13 |
| 3.SETAR m = 3,mL = 3, mH = 2, d=1,nthresh = 1,thDelay = 0 | Entrenamiento | 43976.89 | 479773603905 | 3932570524 | 62710.21 |
| 2.SETAR m = 3,mL = 2, mH = 3, d=2,nthresh = 1,thDelay = 2 | Entrenamiento | 51081.82 | 607319064278 | 4978025117 | 70555.12 |
| 4.SETAR m = 3,mL = 1, mH = 2, d=2,nthresh = 1,thDelay = 0 | Entrenamiento | 53854.21 | 669984437781 | 5491675720 | 74105.84 |
| 1.SETAR m = 3,mL = 3, mH = 1, d=1,nthresh = 1,thDelay = 2 | Prueba | 58177.95 | 27099683662 | 5419936732 | 73620.22 |
| 4.SETAR m = 3,mL = 1, mH = 2, d=2,nthresh = 1,thDelay = 0 | Prueba | 70611.86 | 35773255567 | 7154651113 | 84585.17 |
| 2.SETAR m = 3,mL = 2, mH = 3, d=2,nthresh = 1,thDelay = 2 | Prueba | 109019.00 | 80955890516 | 16191178103 | 127244.56 |
| 3.SETAR m = 3,mL = 3, mH = 2, d=1,nthresh = 1,thDelay = 0 | Prueba | 118153.55 | 111351647786 | 22270329557 | 149232.47 |
autoplot(sactnetcrc_train)+
autolayer(fit1)+
autolayer(fit2)+
autolayer(fit3)+
autolayer(fit4)+
theme_bw()
autoplot(sactnetcrc_test)+
autolayer(pronsetar1)+
autolayer(pronsetar2)+
autolayer(pronsetar3)+
autolayer(pronsetar4)+
theme_bw()+
scale_y_continuous(limits = c(500000,1400000))
Metricas Generales
rbind(MetricasTARCRC,
MetricasSETARCRC)%>%
arrange(DataSet,RMSE)%>%
knitr::kable(caption="Metricas de Rendimiento Modelos No Lineales Colones")| Modelo | DataSet | MAE | RSS | MSE | RMSE |
|---|---|---|---|---|---|
| 3.TAR p1=3,p2=3,d=1 | Entrenamiento | 39772.97 | 328942351055 | 2696248779 | 51925.42 |
| 1.TAR p1=3,p2=2,d=1 | Entrenamiento | 40308.12 | 338913405336 | 2777978732 | 52706.53 |
| 2.TAR p1=3,p2=1,d=2 | Entrenamiento | 40514.30 | 351547834112 | 2881539624 | 53679.97 |
| 1.SETAR m = 3,mL = 3, mH = 1, d=1,nthresh = 1,thDelay = 2 | Entrenamiento | 42575.25 | 411939644750 | 3376554465 | 58108.13 |
| 3.SETAR m = 3,mL = 3, mH = 2, d=1,nthresh = 1,thDelay = 0 | Entrenamiento | 43976.89 | 479773603905 | 3932570524 | 62710.21 |
| 2.SETAR m = 3,mL = 2, mH = 3, d=2,nthresh = 1,thDelay = 2 | Entrenamiento | 51081.82 | 607319064278 | 4978025117 | 70555.12 |
| 4.SETAR m = 3,mL = 1, mH = 2, d=2,nthresh = 1,thDelay = 0 | Entrenamiento | 53854.21 | 669984437781 | 5491675720 | 74105.84 |
| 1.SETAR m = 3,mL = 3, mH = 1, d=1,nthresh = 1,thDelay = 2 | Prueba | 58177.95 | 27099683662 | 5419936732 | 73620.22 |
| 3.TAR p1=3,p2=3,d=1 | Prueba | 59009.97 | 30898688005 | 6179737601 | 78611.31 |
| 4.SETAR m = 3,mL = 1, mH = 2, d=2,nthresh = 1,thDelay = 0 | Prueba | 70611.86 | 35773255567 | 7154651113 | 84585.17 |
| 1.TAR p1=3,p2=2,d=1 | Prueba | 84725.95 | 62293400375 | 12458680075 | 111618.46 |
| 2.TAR p1=3,p2=1,d=2 | Prueba | 87059.11 | 64393263703 | 12878652741 | 113484.15 |
| 2.SETAR m = 3,mL = 2, mH = 3, d=2,nthresh = 1,thDelay = 2 | Prueba | 109019.00 | 80955890516 | 16191178103 | 127244.56 |
| 3.SETAR m = 3,mL = 3, mH = 2, d=1,nthresh = 1,thDelay = 0 | Prueba | 118153.55 | 111351647786 | 22270329557 | 149232.47 |
autoplot(sactnetcrc_test)+
autolayer(prontar3)+
autolayer(pronsetar1)+
autolayer(prontar1)+
autolayer(pronsetar4)+
theme_bw()+
scale_y_continuous(limits = c(500000,1400000))
Serie en Dolares
TAR
# m orden
pm <- 1:4
mod.list.tar<-list()
AIC.best.list<-list()
AICM = NULL
model.best <- list(d=0, p1=0, p2=0)
AIC.best = 10000
for(l in pm){
for(j in pm){
for(i in pm){
set.seed(777)
model.tar.s = tar(sactnetusd_train,p1=j,p2=i,d=l)
mod.list.tar[[paste(j,i,l,sep="-")]]<-model.tar.s$AIC
print(paste("Modelo:",j,i,l,sep="-"))
if (model.tar.s$AIC < AIC.best) {
AIC.best = model.tar.s$AIC
AIC.best.list[[paste(j,i,l,sep="-")]]<-AIC.best
#print("Modelo:",j,i,l,"AIC",AIC.best)
model.best$d = l
model.best$p1 = model.tar.s$p1
model.best$p2 = model.tar.s$p2
print(paste(model.tar.s$p1,model.tar.s$p2,l,sep="-")) }
}
}
}## [1] "Modelo:-1-1-1"
## [1] "0-1-1"
## [1] "Modelo:-1-2-1"
## [1] "0-1-1"
## [1] "Modelo:-1-3-1"
## [1] "0-1-1"
## [1] "Modelo:-1-4-1"
## [1] "0-1-1"
## [1] "Modelo:-2-1-1"
## [1] "Modelo:-2-2-1"
## [1] "Modelo:-2-3-1"
## [1] "Modelo:-2-4-1"
## [1] "2-1-1"
## [1] "Modelo:-3-1-1"
## [1] "Modelo:-3-2-1"
## [1] "Modelo:-3-3-1"
## [1] "Modelo:-3-4-1"
## [1] "3-1-1"
## [1] "Modelo:-4-1-1"
## [1] "Modelo:-4-2-1"
## [1] "Modelo:-4-3-1"
## [1] "Modelo:-4-4-1"
## [1] "Modelo:-1-1-2"
## [1] "Modelo:-1-2-2"
## [1] "Modelo:-1-3-2"
## [1] "Modelo:-1-4-2"
## [1] "Modelo:-2-1-2"
## [1] "Modelo:-2-2-2"
## [1] "Modelo:-2-3-2"
## [1] "Modelo:-2-4-2"
## [1] "Modelo:-3-1-2"
## [1] "Modelo:-3-2-2"
## [1] "Modelo:-3-3-2"
## [1] "Modelo:-3-4-2"
## [1] "Modelo:-4-1-2"
## [1] "Modelo:-4-2-2"
## [1] "Modelo:-4-3-2"
## [1] "Modelo:-4-4-2"
## [1] "Modelo:-1-1-3"
## [1] "Modelo:-1-2-3"
## [1] "Modelo:-1-3-3"
## [1] "Modelo:-1-4-3"
## [1] "Modelo:-2-1-3"
## [1] "Modelo:-2-2-3"
## [1] "Modelo:-2-3-3"
## [1] "Modelo:-2-4-3"
## [1] "Modelo:-3-1-3"
## [1] "Modelo:-3-2-3"
## [1] "Modelo:-3-3-3"
## [1] "Modelo:-3-4-3"
## [1] "Modelo:-4-1-3"
## [1] "Modelo:-4-2-3"
## [1] "Modelo:-4-3-3"
## [1] "Modelo:-4-4-3"
## [1] "Modelo:-1-1-4"
## [1] "Modelo:-1-2-4"
## [1] "Modelo:-1-3-4"
## [1] "Modelo:-1-4-4"
## [1] "Modelo:-2-1-4"
## [1] "Modelo:-2-2-4"
## [1] "Modelo:-2-3-4"
## [1] "Modelo:-2-4-4"
## [1] "Modelo:-3-1-4"
## [1] "Modelo:-3-2-4"
## [1] "Modelo:-3-3-4"
## [1] "Modelo:-3-4-4"
## [1] "Modelo:-4-1-4"
## [1] "Modelo:-4-2-4"
## [1] "Modelo:-4-3-4"
## [1] "Modelo:-4-4-4"# AICTar<-bind_rows(mod.list.tar,.id = "Ordene-delay")%>%
# arrange(`1`)
#
# knitr::kable(head(AICTar,20))
AICTarBest<-bind_rows(AIC.best.list,.id = "Ordene-delay")%>%
arrange(`1`)
knitr::kable(head(AICTarBest,20))| Ordene-delay | 1 |
|---|---|
| 3-4-1 | 1349 |
| 2-4-1 | 1350 |
| 1-4-1 | 1360 |
| 1-3-1 | 1372 |
| 1-2-1 | 1382 |
| 1-1-1 | 1394 |
mod.tar1.usd<-TSA::tar(sactnetusd_train,p1=3,p2=4,d=1)
mod.tar2.usd<-TSA::tar(sactnetusd_train,p1=1,p2=2,d=1)
mod.tar3.usd<-TSA::tar(sactnetusd_train,p1=1,p2=3,d=1)
mod.tar1.usd$thd##
## 622.0209mod.tar2.usd$thd##
## 590.6428mod.tar3.usd$thd##
## 590.6428mod.tar1.usd$qr1$coefficients## intercept-sactnetusd_train lag1-sactnetusd_train
## 181.4332347 1.6151098
## lag2-sactnetusd_train lag3-sactnetusd_train
## -1.3676001 0.4599062mod.tar2.usd$qr1$coefficients## intercept-sactnetusd_train
## 569.0528mod.tar3.usd$qr1$coefficients## intercept-sactnetusd_train
## 569.0528mod.tar1.usd$qr2$coefficients## intercept-sactnetusd_train lag1-sactnetusd_train
## 41.7045508 0.9679892mod.tar2.usd$qr2$coefficients## intercept-sactnetusd_train lag1-sactnetusd_train
## 36.6212186 0.9721871mod.tar3.usd$qr2$coefficients## intercept-sactnetusd_train lag1-sactnetusd_train
## 32.8396744 0.9753914data.frame(
Modelo=c("1. TAR p1=3,p2=4,d=1",
"2. TAR p1=1,p2=2,d=1",
"3. TAR p1=1,p2=3,d=1"),
AIC=c(mod.tar1.usd$AIC,
mod.tar2.usd$AIC,
mod.tar3.usd$AIC))%>%
arrange(AIC)%>%
knitr::kable()| Modelo | AIC |
|---|---|
| 1. TAR p1=3,p2=4,d=1 | 1349 |
| 3. TAR p1=1,p2=3,d=1 | 1372 |
| 2. TAR p1=1,p2=2,d=1 | 1382 |
tsdiag(mod.tar1.usd)
tsdiag(mod.tar2.usd)
tsdiag(mod.tar3.usd)
checkresiduals(ts(mod.tar1.usd$residuals,start=inicio_train,frequency = 12))
checkresiduals(ts(mod.tar2.usd$residuals,start=inicio_train,frequency = 12))
checkresiduals(ts(mod.tar3.usd$residuals,start=inicio_train,frequency = 12))
prontar1.usd<- ts(as.vector(predict(mod.tar1.usd,n.ahead=h.param,n.sim=1000)$fit),start=inicio_test,frequency = 12)
prontar2.usd<- ts(as.vector(predict(mod.tar2.usd,n.ahead=h.param,n.sim=1000)$fit),start=inicio_test,frequency = 12)
prontar3.usd<- ts(as.vector(predict(mod.tar3.usd,n.ahead=h.param,n.sim=1000)$fit),start=inicio_test,frequency = 12)
fit1.usd<-ts(as.vector(mod.tar1.usd$y)-as.vector(mod.tar1.usd$residuals),start =inicio_train,frequency = 12)
fit2.usd<-ts(as.vector(mod.tar1.usd$y)-mod.tar2.usd$residuals,start =inicio_train,frequency = 12)
fit3.usd<-ts(as.vector(mod.tar1.usd$y)-mod.tar3.usd$residuals,start =inicio_train,frequency = 12)MetricasTARUSD<-data.frame(
Modelo=rep(c("1. TAR p1=3,p2=4,d=1",
"2. TAR p1=1,p2=2,d=1",
"3. TAR p1=1,p2=3,d=1"),2),
DataSet= c(rep("Entrenamiento",3),rep("Prueba",3)),
rbind(getPerformance(fit1.usd,sactnetusd_train),
getPerformance(fit2.usd,sactnetusd_train),
getPerformance(fit3.usd,sactnetusd_train),
getPerformance(prontar1.usd,sactnetusd_test),
getPerformance(prontar2.usd,sactnetusd_test),
getPerformance(prontar3.usd,sactnetusd_test)))%>%
arrange(DataSet,RMSE)
MetricasTARUSD%>%
knitr::kable(caption="Metricas de Rendimiento Modelos TAR USD")| Modelo | DataSet | MAE | RSS | MSE | RMSE |
|---|---|---|---|---|---|
| 1. TAR p1=3,p2=4,d=1 | Entrenamiento | 55.96542 | 676293.4 | 5543.389 | 74.45394 |
| 3. TAR p1=1,p2=3,d=1 | Entrenamiento | 59.12611 | 711230.7 | 5829.760 | 76.35286 |
| 2. TAR p1=1,p2=2,d=1 | Entrenamiento | 59.42877 | 713711.6 | 5850.095 | 76.48592 |
| 3. TAR p1=1,p2=3,d=1 | Prueba | 121.80264 | 106912.6 | 21382.522 | 146.22764 |
| 2. TAR p1=1,p2=2,d=1 | Prueba | 125.33080 | 113962.3 | 22792.469 | 150.97175 |
| 1. TAR p1=3,p2=4,d=1 | Prueba | 131.18617 | 124281.3 | 24856.252 | 157.65866 |
autoplot(sactnetusd_train)+
autolayer(fit1.usd)+
autolayer(fit2.usd)+
autolayer(fit3.usd)+
theme_bw()
autoplot(sactnetusd_test)+
autolayer(prontar1.usd)+
autolayer(prontar2.usd)+
autolayer(prontar3.usd)+
theme_bw()
SETAR
Thus the threshold delay, the number of lags in each regime and the threshold value are computed.
Setar1.usd <-
selectSETAR(
sactnetusd_train,
include = c("const", "trend","none", "both"),
m = 4,
thDelay = seq(0, 3, by = 1),
nthresh = 3,
criterion = "AIC",
type = "level",
plot = T,
trace = T
)## Using maximum autoregressive order for low regime: mL = 4
## Using maximum autoregressive order for high regime: mH = 4
## Searching on 82 possible threshold values within regimes with sufficient ( 15% ) number of observations
## Searching on 5248 combinations of thresholds ( 82 ), thDelay ( 4 ), mL ( 4 ) and MM ( 4 )
##
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
Setar2.usd <-
selectSETAR(
sactnetusd_train,
m = 4,
d=2,
thDelay = seq(0, 3, by = 1),
nthresh = 3,
criterion = "AIC",
type = "level",
plot = T,
trace = T
)## Using maximum autoregressive order for low regime: mL = 4
## Using maximum autoregressive order for high regime: mH = 4
## Searching on 78 possible threshold values within regimes with sufficient ( 15% ) number of observations
## Searching on 4992 combinations of thresholds ( 78 ), thDelay ( 4 ), mL ( 4 ) and MM ( 4 )
##
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
Setar3.usd <-
selectSETAR(
sactnetusd_train,
m = 4,
thDelay = seq(0, 3, by = 1),
nthresh = 3,
d = 1,
criterion = "AIC",
type = "level",
plot = T,
trace = T
)## Using maximum autoregressive order for low regime: mL = 4
## Using maximum autoregressive order for high regime: mH = 4
## Searching on 82 possible threshold values within regimes with sufficient ( 15% ) number of observations
## Searching on 5248 combinations of thresholds ( 82 ), thDelay ( 4 ), mL ( 4 ) and MM ( 4 )
##
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1212.776
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
## 1 T: Trim not respected: 0.8559322 0.1440678 from th: 1214.61
## 1 T: Trim not respected: 0.8644068 0.1355932 from th: 1214.61
## 1 T: Trim not respected: 0.8728814 0.1271186 from th: 1214.61
Setar4.usd <-
selectSETAR(
sactnetusd_train,
m = 4,
thDelay = seq(0, 3, by = 1),
nthresh = 3,
d = 2,
criterion = "AIC",
type = "level",
plot = T,
trace = T
)## Using maximum autoregressive order for low regime: mL = 4
## Using maximum autoregressive order for high regime: mH = 4
## Searching on 78 possible threshold values within regimes with sufficient ( 15% ) number of observations
## Searching on 4992 combinations of thresholds ( 78 ), thDelay ( 4 ), mL ( 4 ) and MM ( 4 )
##
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1207.644
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1208.27
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8508772 0.1491228 from th: 1212.131
## 1 T: Trim not respected: 0.8684211 0.1315789 from th: 1212.131
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1212.381
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1212.381
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
## 1 T: Trim not respected: 0.8596491 0.1403509 from th: 1213.601
## 1 T: Trim not respected: 0.877193 0.122807 from th: 1213.601
## 1 T: Trim not respected: 0.8947368 0.1052632 from th: 1213.601
Setar1.usd$allTh%>%
as.data.frame()%>%
arrange(AIC,thDelay,mL,mH)%>%
head(5)## thDelay mL mH th AIC
## 1 1 1 1 1212.776 1055.814
## 2 1 1 1 1202.790 1056.177
## 3 1 2 1 1212.776 1056.251
## 4 1 2 1 1202.790 1056.635
## 5 1 1 1 1208.270 1056.776Setar2.usd$allTh%>%
as.data.frame()%>%
arrange(AIC,thDelay,mL,mH)%>%
head(5)## thDelay mL mH th AIC
## 1 0 1 4 1182.401 1119.110
## 2 0 1 3 1182.401 1119.187
## 3 0 1 4 1191.128 1119.788
## 4 0 1 4 1193.428 1119.823
## 5 0 1 3 1193.428 1119.826Setar3.usd$allTh%>%
as.data.frame()%>%
arrange(AIC,thDelay,mL,mH)%>%
head(5)## thDelay mL mH th AIC
## 1 1 1 1 1212.776 1055.814
## 2 1 1 1 1202.790 1056.177
## 3 1 2 1 1212.776 1056.251
## 4 1 2 1 1202.790 1056.635
## 5 1 1 1 1208.270 1056.776Setar4.usd$allTh%>%
as.data.frame()%>%
arrange(AIC,thDelay,mL,mH)%>%
head(5)## thDelay mL mH th AIC
## 1 0 1 4 1182.401 1119.110
## 2 0 1 3 1182.401 1119.187
## 3 0 1 4 1191.128 1119.788
## 4 0 1 4 1193.428 1119.823
## 5 0 1 3 1193.428 1119.826modeloas1.usd <-
setar(
sactnetusd_train,
mL = 1,
mH = 1,
d=1,
nthresh = 1,
thDelay = 2,
type = "level"
)##
## 1 T: Trim not respected: 0.8655462 0.1344538 from th: 1214.61## Raiz Unitaria
summary(modeloas1.usd) #residuals variance = 0.005525, AIC = -632, MAPE = 0.4352%##
## Non linear autoregressive model
##
## SETAR model ( 2 regimes)
## Coefficients:
## Low regime:
## const.L phiL.1
## 149.9768322 0.7745225
##
## High regime:
## const.H phiH.1
## 97.1339609 0.9222561
##
## Threshold:
## -Variable: Z(t) = + (0) X(t)+ (0)X(t-1)+ (1)X(t-2)
## -Value: 790.7
## Proportion of points in low regime: 24.37% High regime: 75.63%
##
## Residuals:
## Min 1Q Median 3Q Max
## -189.9204 -49.4084 -6.5524 42.2501 252.5032
##
## Fit:
## residuals variance = 5509, AIC = 1061, MAPE = 5.783%
##
## Coefficient(s):
##
## Estimate Std. Error t value Pr(>|t|)
## const.L 149.976832 88.379070 1.6970 0.09234 .
## phiL.1 0.774522 0.132371 5.8512 0.00000004466 ***
## const.H 97.133961 55.118809 1.7623 0.08061 .
## phiH.1 0.922256 0.048606 18.9740 < 0.00000000000000022 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold
## Variable: Z(t) = + (0) X(t) + (0) X(t-1)+ (1) X(t-2)
##
## Value: 790.7# plot(modeloas1)
checkresiduals(ts(modeloas1.usd$residuals,start=c(2011,1),frequency = 12))
modeloas2.usd <-
setar(
sactnetusd_train,
mL = 1,
mH = 3,
d=2,
nthresh = 1,
thDelay = 1,
type = "level"
)##
## 1 T: Trim not respected: 0.862069 0.137931 from th: 1213.601## Raiz Unitaria
summary(modeloas2.usd) # residuals variance = 0.005857, AIC = -635, MAPE = 0.4584%##
## Non linear autoregressive model
##
## SETAR model ( 2 regimes)
## Coefficients:
## Low regime:
## const.L phiL.1
## 94.1527345 0.9211025
##
## High regime:
## const.H phiH.1 phiH.2 phiH.3
## -318.4527806 0.7902831 -0.2328454 0.7309027
##
## Threshold:
## -Variable: Z(t) = + (0) X(t)+ (1)X(t-1)+ (0)X(t-2)
## -Value: 1208
## Proportion of points in low regime: 82.76% High regime: 17.24%
##
## Residuals:
## Min 1Q Median 3Q Max
## -290.709 -60.694 -11.602 59.844 282.297
##
## Fit:
## residuals variance = 9698, AIC = 1134, MAPE = 8.4%
##
## Coefficient(s):
##
## Estimate Std. Error t value Pr(>|t|)
## const.L 94.152734 44.346631 2.1231 0.0358703 *
## phiL.1 0.921103 0.044864 20.5309 < 0.00000000000000022 ***
## const.H -318.452781 287.963339 -1.1059 0.2710669
## phiH.1 0.790283 0.226575 3.4880 0.0006886 ***
## phiH.2 -0.232845 0.363553 -0.6405 0.5231297
## phiH.3 0.730903 0.216327 3.3787 0.0009924 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold
## Variable: Z(t) = + (0) X(t) + (1) X(t-1)+ (0) X(t-2)
##
## Value: 1208# plot(modeloas2)
checkresiduals(ts(modeloas2.usd$residuals,start=c(2011,1),frequency = 12))
modeloas3.usd <-
setar(
sactnetusd_train,
mL = 1,
mH = 1,
d=1,
nthresh = 1,
thDelay = 1,
type = "level"
)##
## 1 T: Trim not respected: 0.8583333 0.1416667 from th: 1214.61## Raiz Unitaria
summary(modeloas3.usd) # residuals variance = 0.006319, AIC = -621, MAPE = 0.4621%##
## Non linear autoregressive model
##
## SETAR model ( 2 regimes)
## Coefficients:
## Low regime:
## const.L phiL.1
## 26.5631680 0.9873869
##
## High regime:
## const.H phiH.1
## -366.770565 1.258479
##
## Threshold:
## -Variable: Z(t) = + (0) X(t)+ (1)X(t-1)
## -Value: 1213
## Proportion of points in low regime: 84.17% High regime: 15.83%
##
## Residuals:
## Min 1Q Median 3Q Max
## -159.9134 -51.1907 -7.7945 49.2934 246.0615
##
## Fit:
## residuals variance = 5365, AIC = 1058, MAPE = 5.927%
##
## Coefficient(s):
##
## Estimate Std. Error t value Pr(>|t|)
## const.L 26.563168 32.728557 0.8116 0.41864
## phiL.1 0.987387 0.033652 29.3408 < 0.0000000000000002 ***
## const.H -366.770565 154.135328 -2.3795 0.01894 *
## phiH.1 1.258479 0.115936 10.8549 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold
## Variable: Z(t) = + (0) X(t) + (1) X(t-1)
##
## Value: 1213# plot(modeloas3)
checkresiduals(ts(modeloas3.usd$residuals,start=c(2011,1),frequency = 12))
modeloas4.usd <-
setar(
sactnetusd_train,
m = 4,
mL = 1,
mH = 4,
d=2,
nthresh = 1,
thDelay = 0,
type = "level"
)
summary(modeloas4.usd) # residuals variance = 0.006319, AIC = -621, MAPE = 0.4621%##
## Non linear autoregressive model
##
## SETAR model ( 2 regimes)
## Coefficients:
## Low regime:
## const.L phiL.1
## 32.723268 1.005528
##
## High regime:
## const.H phiH.1 phiH.2 phiH.3 phiH.4
## -533.2039159 1.2228038 -0.2014876 0.6895297 -0.3198967
##
## Threshold:
## -Variable: Z(t) = + (1) X(t)+ (0)X(t-1)+ (0)X(t-2)+ (0)X(t-3)
## -Value: 1182
## Proportion of points in low regime: 75.44% High regime: 24.56%
##
## Residuals:
## Min 1Q Median 3Q Max
## -249.0256 -67.8389 6.3576 57.1181 275.2895
##
## Fit:
## residuals variance = 8450, AIC = 1119, MAPE = 7.924%
##
## Coefficient(s):
##
## Estimate Std. Error t value Pr(>|t|)
## const.L 32.723268 48.127028 0.6799 0.4979119
## phiL.1 1.005528 0.050783 19.8003 < 0.00000000000000022 ***
## const.H -533.203916 232.449479 -2.2938 0.0236151 *
## phiH.1 1.222804 0.240614 5.0820 0.00000146 ***
## phiH.2 -0.201488 0.216292 -0.9316 0.3535187
## phiH.3 0.689530 0.203927 3.3813 0.0009865 ***
## phiH.4 -0.319897 0.227650 -1.4052 0.1626545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Threshold
## Variable: Z(t) = + (1) X(t) + (0) X(t-1)+ (0) X(t-2)+ (0) X(t-3)
##
## Value: 1182# plot(modeloas4)
checkresiduals(ts(modeloas4.usd$residuals,start=c(2011,1),frequency = 12))
cbind(
Modelo=c("1.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 2",
"2.SETAR m = 4,mL = 1, mH = 3, d=2,nthresh = 1,thDelay = 2",
"3.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 1",
"4.SETAR m = 4,mL = 1, mH = 4, d=2,nthresh = 1,thDelay = 0"),
AIC=c(
AIC(modeloas1),
AIC(modeloas2),
AIC(modeloas3),
AIC(modeloas4)
),
BIC=c(
BIC(modeloas1),
BIC(modeloas2),
BIC(modeloas3),
BIC(modeloas4)
)
)%>%
knitr::kable()| Modelo | AIC | BIC |
|---|---|---|
| 1.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 2 | 2664.94192921197 | 2684.57007652511 |
| 2.SETAR m = 4,mL = 1, mH = 3, d=2,nthresh = 1,thDelay = 2 | 2715.40394269942 | 2737.83611105729 |
| 3.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 1 | 2667.26111427235 | 2689.69328263021 |
| 4.SETAR m = 4,mL = 1, mH = 4, d=2,nthresh = 1,thDelay = 0 | 2725.78583996026 | 2742.60996622866 |
pronsetar1.usd<- predict(modeloas1.usd, n.ahead = h.param)
pronsetar2.usd<- predict(modeloas2.usd, n.ahead = h.param)
pronsetar3.usd<- predict(modeloas3.usd, n.ahead = h.param)
pronsetar4.usd<- predict(modeloas4.usd, n.ahead = h.param)
fit1.usd<-ts(modeloas1.usd$fitted.values,start =inicio_train,frequency = 12)
fit2.usd<-ts(modeloas2.usd$fitted.values,start =inicio_train,frequency = 12)
fit3.usd<-ts(modeloas3.usd$fitted.values,start =inicio_train,frequency = 12)
fit4.usd<-ts(modeloas4.usd$fitted.values,start =inicio_train,frequency = 12)MetricasSETARUSD<-data.frame(
Modelo=rep(
c("1.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 2",
"2.SETAR m = 4,mL = 1, mH = 3, d=2,nthresh = 1,thDelay = 2",
"3.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 1",
"4.SETAR m = 4,mL = 1, mH = 4, d=2,nthresh = 1,thDelay = 0"),2),
DataSet= c(rep("Entrenamiento",4),rep("Prueba",4)),
rbind(
getPerformance(fit1.usd,sactnetusd_train),
getPerformance(fit2.usd,sactnetusd_train),
getPerformance(fit3.usd,sactnetusd_train),
getPerformance(fit4.usd,sactnetusd_train),
getPerformance(pronsetar1.usd,sactnetusd_test),
getPerformance(pronsetar2.usd,sactnetusd_test),
getPerformance(pronsetar3.usd,sactnetusd_test),
getPerformance(pronsetar4.usd,sactnetusd_test)
))%>%
arrange(DataSet,RMSE)
MetricasSETARUSD%>%
knitr::kable(caption="Metricas de Rendimiento Modelos SETAR")| Modelo | DataSet | MAE | RSS | MSE | RMSE |
|---|---|---|---|---|---|
| 3.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 1 | Entrenamiento | 65.24851 | 867891.56 | 7113.865 | 84.34373 |
| 1.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 2 | Entrenamiento | 76.03300 | 1190732.79 | 9760.105 | 98.79324 |
| 2.SETAR m = 4,mL = 1, mH = 3, d=2,nthresh = 1,thDelay = 2 | Entrenamiento | 98.93892 | 2057277.74 | 16862.932 | 129.85735 |
| 4.SETAR m = 4,mL = 1, mH = 4, d=2,nthresh = 1,thDelay = 0 | Entrenamiento | 120.78529 | 3019923.18 | 24753.469 | 157.33235 |
| 4.SETAR m = 4,mL = 1, mH = 4, d=2,nthresh = 1,thDelay = 0 | Prueba | 53.47200 | 30513.91 | 6102.783 | 78.12031 |
| 2.SETAR m = 4,mL = 1, mH = 3, d=2,nthresh = 1,thDelay = 2 | Prueba | 60.85853 | 42188.91 | 8437.782 | 91.85740 |
| 1.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 2 | Prueba | 176.35296 | 221897.62 | 44379.524 | 210.66448 |
| 3.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 1 | Prueba | 195.36421 | 265812.38 | 53162.477 | 230.56990 |
autoplot(sactnetusd_train)+
autolayer(fit1.usd)+
autolayer(fit2.usd)+
autolayer(fit3.usd)+
autolayer(fit4.usd)+
theme_bw()
autoplot(sactnetusd_test)+
autolayer(pronsetar1.usd)+
autolayer(pronsetar2.usd)+
autolayer(pronsetar3.usd)+
autolayer(pronsetar4.usd)+
theme_bw()
Metricas Generales
rbind(MetricasTARUSD,
MetricasSETARUSD)%>%
arrange(DataSet,RMSE)%>%
knitr::kable(caption="Metricas de Rendimiento Modelos No Lineales Dolares")| Modelo | DataSet | MAE | RSS | MSE | RMSE |
|---|---|---|---|---|---|
| 1. TAR p1=3,p2=4,d=1 | Entrenamiento | 55.96542 | 676293.45 | 5543.389 | 74.45394 |
| 3. TAR p1=1,p2=3,d=1 | Entrenamiento | 59.12611 | 711230.72 | 5829.760 | 76.35286 |
| 2. TAR p1=1,p2=2,d=1 | Entrenamiento | 59.42877 | 713711.62 | 5850.095 | 76.48592 |
| 3.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 1 | Entrenamiento | 65.24851 | 867891.56 | 7113.865 | 84.34373 |
| 1.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 2 | Entrenamiento | 76.03300 | 1190732.79 | 9760.105 | 98.79324 |
| 2.SETAR m = 4,mL = 1, mH = 3, d=2,nthresh = 1,thDelay = 2 | Entrenamiento | 98.93892 | 2057277.74 | 16862.932 | 129.85735 |
| 4.SETAR m = 4,mL = 1, mH = 4, d=2,nthresh = 1,thDelay = 0 | Entrenamiento | 120.78529 | 3019923.18 | 24753.469 | 157.33235 |
| 4.SETAR m = 4,mL = 1, mH = 4, d=2,nthresh = 1,thDelay = 0 | Prueba | 53.47200 | 30513.91 | 6102.783 | 78.12031 |
| 2.SETAR m = 4,mL = 1, mH = 3, d=2,nthresh = 1,thDelay = 2 | Prueba | 60.85853 | 42188.91 | 8437.782 | 91.85740 |
| 3. TAR p1=1,p2=3,d=1 | Prueba | 121.80264 | 106912.61 | 21382.522 | 146.22764 |
| 2. TAR p1=1,p2=2,d=1 | Prueba | 125.33080 | 113962.34 | 22792.469 | 150.97175 |
| 1. TAR p1=3,p2=4,d=1 | Prueba | 131.18617 | 124281.26 | 24856.252 | 157.65866 |
| 1.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 2 | Prueba | 176.35296 | 221897.62 | 44379.524 | 210.66448 |
| 3.SETAR m = 4,mL = 1, mH = 1, d=1,nthresh = 1,thDelay = 1 | Prueba | 195.36421 | 265812.38 | 53162.477 | 230.56990 |
autoplot(sactnetusd_test)+
autolayer(prontar1.usd)+
autolayer(pronsetar1.usd)+
theme_bw()
3.2.3. Modelo Machine Learning
# Machine Learning
library(tidymodels)
library(modeltime)
library(modeltime.ensemble)
library(modeltime.resample)
library(timetk)
library(tidyverse)Serie en Colones
colones%>%
plot_time_series(Date,value,.facet_ncol = 3, .interactive = F)DATA PREPARATION
FORECAST_HORIZON <- h.param
FORECAST_HORIZON <- 5Full = Training + Forecast Dataset
full_data_tbl <- colones%>%
select(Date,value)%>%
future_frame(
.date_var = Date,
.length_out = FORECAST_HORIZON,
.bind_data = T
)Training Data
data_prepared_tbl <- full_data_tbl[!is.na(full_data_tbl$value),]
# data_prepared_tbl%>%
# tk_summary_diagnostics()Future Data Forecast
future_tbl <- full_data_tbl[is.na(full_data_tbl$value),]SPLITTING
splits <- data_prepared_tbl%>%
arrange(Date)%>%
time_series_split(
data_var=Date,
assess = FORECAST_HORIZON,
cumulative = T
)
splits## <Analysis/Assess/Total>
## <241/5/246>dim(training(splits))## [1] 241 2dim(testing(splits))## [1] 5 2PREPROCESOR
recipe_spec_1 <- recipe(value~., training(splits))%>%
step_timeseries_signature(Date)%>%
## Elimina las columnas o atributos que no aportan
step_rm(matches("(.iso$)|(.xts)|(day)|(hour)|(minute)|(second)|(am.pm)|(week)")) %>%
step_normalize(Date_index.num,Date_year)%>%
step_mutate(Date_month = factor(Date_month,ordered = T))%>%
step_dummy(all_nominal(),one_hot = T)
recipe_spec_1 %>% prep() %>% juice() %>% glimpse()## Rows: 241
## Columns: 30
## $ Date <date> 2001-02-01, 2001-03-01, 2001-04-01, 2001-05-01, 200…
## $ value <dbl> 12637.44, 13569.26, 11895.00, 12882.29, 13393.90, 11…
## $ Date_index.num <dbl> -1.720507, -1.707312, -1.692703, -1.678565, -1.66395…
## $ Date_year <dbl> -1.656391, -1.656391, -1.656391, -1.656391, -1.65639…
## $ Date_half <int> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2…
## $ Date_quarter <int> 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3…
## $ Date_month_01 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ Date_month_02 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ Date_month_03 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
## $ Date_month_04 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
## $ Date_month_05 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ Date_month_06 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ Date_month_07 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ Date_month_08 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_09 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_01 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_02 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ Date_month.lbl_03 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
## $ Date_month.lbl_04 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
## $ Date_month.lbl_05 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ Date_month.lbl_06 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ Date_month.lbl_07 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ Date_month.lbl_08 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_09 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…recipe_spec_2 <- recipe_spec_1%>%
update_role(Date,new_role = "ID")
recipe_spec_2 %>% prep() %>% juice() %>% glimpse()## Rows: 241
## Columns: 30
## $ Date <date> 2001-02-01, 2001-03-01, 2001-04-01, 2001-05-01, 200…
## $ value <dbl> 12637.44, 13569.26, 11895.00, 12882.29, 13393.90, 11…
## $ Date_index.num <dbl> -1.720507, -1.707312, -1.692703, -1.678565, -1.66395…
## $ Date_year <dbl> -1.656391, -1.656391, -1.656391, -1.656391, -1.65639…
## $ Date_half <int> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2…
## $ Date_quarter <int> 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3…
## $ Date_month_01 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ Date_month_02 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ Date_month_03 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
## $ Date_month_04 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
## $ Date_month_05 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ Date_month_06 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ Date_month_07 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ Date_month_08 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_09 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_01 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_02 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ Date_month.lbl_03 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
## $ Date_month.lbl_04 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
## $ Date_month.lbl_05 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ Date_month.lbl_06 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ Date_month.lbl_07 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ Date_month.lbl_08 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_09 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…recipe_spec_1 %>% prep() %>% summary()## # A tibble: 30 × 4
## variable type role source
## <chr> <chr> <chr> <chr>
## 1 Date date predictor original
## 2 value numeric outcome original
## 3 Date_index.num numeric predictor derived
## 4 Date_year numeric predictor derived
## 5 Date_half numeric predictor derived
## 6 Date_quarter numeric predictor derived
## 7 Date_month_01 numeric predictor derived
## 8 Date_month_02 numeric predictor derived
## 9 Date_month_03 numeric predictor derived
## 10 Date_month_04 numeric predictor derived
## # … with 20 more rowsrecipe_spec_2 %>% prep() %>% summary()## # A tibble: 30 × 4
## variable type role source
## <chr> <chr> <chr> <chr>
## 1 Date date ID original
## 2 value numeric outcome original
## 3 Date_index.num numeric predictor derived
## 4 Date_year numeric predictor derived
## 5 Date_half numeric predictor derived
## 6 Date_quarter numeric predictor derived
## 7 Date_month_01 numeric predictor derived
## 8 Date_month_02 numeric predictor derived
## 9 Date_month_03 numeric predictor derived
## 10 Date_month_04 numeric predictor derived
## # … with 20 more rowsMODELS
XGBOOST
wflw_fit_xgboost_0_015 <- workflow()%>%
add_model(
boost_tree(learn_rate=0.15) %>% set_engine("xgboost")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))
wflw_fit_xgboost_0_1 <- workflow()%>%
add_model(
boost_tree(learn_rate=0.1) %>% set_engine("xgboost")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))
wflw_fit_xgboost_0_3 <- workflow()%>%
add_model(
boost_tree(learn_rate=0.3) %>% set_engine("xgboost")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))Random Forest
wflw_fit_rf_1000 <- workflow()%>%
add_model(
rand_forest(
trees = 1000
) %>%
set_engine("randomForest")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))
wflw_fit_rf_500 <- workflow()%>%
add_model(
rand_forest(
trees = 500
) %>%
set_engine("randomForest")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))
wflw_fit_rf_200 <- workflow()%>%
add_model(
rand_forest(
trees = 200
) %>%
set_engine("randomForest")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))SVM
wflw_fit_svm <- workflow()%>%
add_model(
svm_rbf() %>% set_engine("kernlab")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))prophet_boost
wflw_fit_prophet_boost <- workflow()%>%
add_model(
prophet_boost(
seasonality_yearly = F,
seasonality_weekly = F,
seasonality_daily = F,
) %>%
set_engine("prophet_xgboost")
)%>%
add_recipe(recipe_spec_1)%>%
fit(training(splits))MODELTIME WORKFLOW
modeltime table
submodels_tbl <- modeltime_table(
# wflw_fit_prophet, #1
wflw_fit_prophet_boost, #2
#wflw_fit_xgboost_0_015, #3
#wflw_fit_xgboost_0_1, #4
wflw_fit_xgboost_0_3, #5
#wflw_fit_rf_1000, #6
wflw_fit_rf_500 , #7
#wflw_fit_rf_200, #8
wflw_fit_svm #9
)
submodels_tbl## # Modeltime Table
## # A tibble: 4 × 3
## .model_id .model .model_desc
## <int> <list> <chr>
## 1 1 <workflow> PROPHET W/ XGBOOST ERRORS
## 2 2 <workflow> XGBOOST
## 3 3 <workflow> RANDOMFOREST
## 4 4 <workflow> KERNLABcalibrate Testing Data
submodels_calibrated_tbl_train <- submodels_tbl %>%
modeltime_calibrate(training(splits))
submodels_calibrated_tbl_train## # Modeltime Table
## # A tibble: 4 × 5
## .model_id .model .model_desc .type .calibration_data
## <int> <list> <chr> <chr> <list>
## 1 1 <workflow> PROPHET W/ XGBOOST ERRORS Fitted <tibble [241 × 4]>
## 2 2 <workflow> XGBOOST Test <tibble [241 × 4]>
## 3 3 <workflow> RANDOMFOREST Test <tibble [241 × 4]>
## 4 4 <workflow> KERNLAB Test <tibble [241 × 4]>submodels_calibrated_tbl <- submodels_tbl %>%
modeltime_calibrate(testing(splits))
submodels_calibrated_tbl## # Modeltime Table
## # A tibble: 4 × 5
## .model_id .model .model_desc .type .calibration_data
## <int> <list> <chr> <chr> <list>
## 1 1 <workflow> PROPHET W/ XGBOOST ERRORS Test <tibble [5 × 4]>
## 2 2 <workflow> XGBOOST Test <tibble [5 × 4]>
## 3 3 <workflow> RANDOMFOREST Test <tibble [5 × 4]>
## 4 4 <workflow> KERNLAB Test <tibble [5 × 4]>Measure Test Accuracy
submodels_calibrated_tbl_train%>%
modeltime_accuracy()%>%
arrange(rmse)%>%
knitr::kable()| .model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
|---|---|---|---|---|---|---|---|---|
| 1 | PROPHET W/ XGBOOST ERRORS | Fitted | 8358.066 | 4.664870 | 0.2878886 | 5.034977 | 12284.70 | 0.9982249 |
| 2 | XGBOOST | Test | 9057.424 | 2.410889 | 0.3119775 | 2.426319 | 13862.23 | 0.9979730 |
| 3 | RANDOMFOREST | Test | 22228.350 | 27.626853 | 0.7656422 | 17.175190 | 28609.14 | 0.9937744 |
| 4 | KERNLAB | Test | 51133.042 | 27.917171 | 1.7612471 | 25.196393 | 74180.79 | 0.9447666 |
submodels_calibrated_tbl%>%
modeltime_accuracy()%>%
arrange(rmse)%>%
knitr::kable()| .model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
|---|---|---|---|---|---|---|---|---|
| 1 | PROPHET W/ XGBOOST ERRORS | Test | 52131.10 | 5.268918 | 0.9106689 | 5.210271 | 64435.97 | 0.9630310 |
| 2 | XGBOOST | Test | 46463.83 | 4.473893 | 0.8116684 | 4.667059 | 66341.33 | 0.5212406 |
| 3 | RANDOMFOREST | Test | 60557.75 | 5.825055 | 1.0578725 | 6.073960 | 74428.34 | 0.9052646 |
| 4 | KERNLAB | Test | 199407.40 | 19.831176 | 3.4834126 | 22.083500 | 205148.12 | 0.9719017 |
Visualize test forecast
Predict_ML_CR_train <- submodels_calibrated_tbl_train %>%
modeltime_forecast(
new_data = training(splits),
actual_data = training(splits),
keep_data = F
)
Predict_ML_CR_test <- submodels_calibrated_tbl %>%
modeltime_forecast(
new_data = testing(splits),
actual_data = testing(splits),
keep_data = F
)
Predict_ML_CR_train%>%
plot_modeltime_forecast(
.facet_ncol=2
)Predict_ML_CR_test%>%
plot_modeltime_forecast(
.facet_ncol=2
)Refit on full training dataset
submodels_refit_tbl <- submodels_calibrated_tbl %>%
modeltime_refit(data_prepared_tbl)Visualize Submodel Forecast
Predict_ML_CR_full <- submodels_refit_tbl%>%
modeltime_forecast(
new_data = future_tbl,
actual_data = data_prepared_tbl,
keep_data = T
)
DT::datatable(Predict_ML_CR_full)Predict_ML_CR_full%>%
plot_modeltime_forecast(
.facet_ncol=2
)Validación cruzada
https://cran.r-project.org/web/packages/modeltime.resample/vignettes/getting-started.html
resamples_tscv <- time_series_cv(
data = data_prepared_tbl,
date_var = Date,
assess = FORECAST_HORIZON,
initial = "36 month",
skip = FORECAST_HORIZON,
slice_limit = 5
)
resamples_tscv## # Time Series Cross Validation Plan
## # A tibble: 5 × 2
## splits id
## <list> <chr>
## 1 <split [36/5]> Slice1
## 2 <split [36/5]> Slice2
## 3 <split [36/5]> Slice3
## 4 <split [36/5]> Slice4
## 5 <split [36/5]> Slice5resamples_tscv %>%
tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(Date,
value,
.facet_ncol = 2,
.interactive = T)Generate Resample Predictions
resamples_fitted <- submodels_tbl %>%
modeltime_fit_resamples(
resamples = resamples_tscv,
control = control_resamples(verbose = FALSE)
)
resamples_fitted## # Modeltime Table
## # A tibble: 4 × 4
## .model_id .model .model_desc .resample_results
## <int> <list> <chr> <list>
## 1 1 <workflow> PROPHET W/ XGBOOST ERRORS <rsmp[+]>
## 2 2 <workflow> XGBOOST <rsmp[+]>
## 3 3 <workflow> RANDOMFOREST <rsmp[+]>
## 4 4 <workflow> KERNLAB <rsmp[+]>Evaluate the Results
resamples_fitted %>%
plot_modeltime_resamples(
.point_size = 3,
.point_alpha = 0.8,
.interactive = T
)resamples_fitted %>%
modeltime_resample_accuracy(summary_fns = mean) %>%
table_modeltime_accuracy(.interactive = T)ENSEMBLE
Ensamble Media y Meta-Learner
ensemble_fit_mean <- submodels_tbl %>%
#filter(!.model_id %in% c(1))%>%
ensemble_average(type="mean")
ensemble_fit_lm <- resamples_fitted %>%
ensemble_model_spec(
model_spec = linear_reg(
penalty = tune(),
mixture = tune()
) %>%
set_engine("glmnet"),
grid = 2,
control = control_grid(verbose = TRUE)
)## ── Tuning Model Specification ───────────────────────────────────
##
##
## # A tibble: 1 × 8
## penalty mixture .metric .estimator mean n std_err .config
## <dbl> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 2.14e-10 0.314 rmse standard 103284. 5 14454. Preprocessor1_Model1
##
## # A tibble: 5 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 116572. PROPHET W/ XGBOOST ERRORS
## 2 2 121334. XGBOOST
## 3 3 127938. RANDOMFOREST
## 4 4 196440. KERNLAB
## 5 ensemble 90419. ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
##
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: linear_reg()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 0 Recipe Steps
##
## ── Model ───────────────────────────────────────────────────────────────────────
##
## Call: glmnet::glmnet(x = maybe_matrix(x), y = y, family = "gaussian", alpha = ~0.314300204394385)
##
## Df %Dev Lambda
## 1 0 0.00 315100
## 2 3 5.63 287100
## 3 3 11.33 261600
## 4 3 16.50 238400
## 5 4 21.19 217200
## 6 4 25.70 197900
## 7 4 29.69 180300
## 8 4 33.19 164300
## 9 4 36.25 149700
## 10 4 38.93 136400
## 11 4 41.25 124300
## 12 4 43.26 113200
## 13 4 44.99 103200
## 14 4 46.49 94020
## 15 4 47.77 85670
## 16 4 48.87 78060
## 17 4 49.81 71120
## 18 4 50.61 64800
## 19 4 51.29 59050
## 20 4 51.87 53800
## 21 4 52.36 49020
## 22 4 52.78 44670
## 23 4 53.13 40700
## 24 4 53.43 37080
## 25 4 53.68 33790
## 26 4 53.90 30790
## 27 4 54.08 28050
## 28 4 54.23 25560
## 29 4 54.36 23290
## 30 4 54.48 21220
## 31 4 54.57 19340
## 32 4 54.65 17620
## 33 4 54.73 16050
## 34 4 54.79 14630
## 35 4 54.84 13330
## 36 4 54.89 12140
## 37 4 54.93 11060
## 38 4 54.97 10080
## 39 4 55.00 9186
## 40 4 55.04 8370
## 41 4 55.07 7626
## 42 4 55.09 6949
## 43 4 55.12 6331
## 44 4 55.14 5769
## 45 4 55.17 5256
## 46 4 55.19 4790
##
## ...
## and 49 more lines.
##
## 8.632 sec elapsedensemble_fit_xg<- resamples_fitted %>%
ensemble_model_spec(
model_spec = boost_tree(
mtry=tune(),
trees=tune(),
learn_rate = tune()
) %>% set_engine("xgboost"),
control = control_grid(verbose = TRUE)
)## ── Tuning Model Specification ───────────────────────────────────
##
##
## # A tibble: 1 × 9
## mtry trees learn_rate .metric .estimator mean n std_err .config
## <int> <int> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 4 1843 0.00432 rmse standard 81661. 5 10948. Preprocessor1_…
##
## # A tibble: 5 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 116572. PROPHET W/ XGBOOST ERRORS
## 2 2 121334. XGBOOST
## 3 3 127938. RANDOMFOREST
## 4 4 196440. KERNLAB
## 5 ensemble 4156. ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
##
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: boost_tree()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 0 Recipe Steps
##
## ── Model ───────────────────────────────────────────────────────────────────────
## ##### xgb.Booster
## raw: 2.3 Mb
## call:
## xgboost::xgb.train(params = list(eta = 0.00431527960951399, max_depth = 6,
## gamma = 0, colsample_bytree = 1, colsample_bynode = 1, min_child_weight = 1,
## subsample = 1, objective = "reg:squarederror"), data = x$data,
## nrounds = 1843L, watchlist = x$watchlist, verbose = 0, nthread = 1)
## params (as set within xgb.train):
## eta = "0.00431527960951399", max_depth = "6", gamma = "0", colsample_bytree = "1", colsample_bynode = "1", min_child_weight = "1", subsample = "1", objective = "reg:squarederror", nthread = "1", validate_parameters = "TRUE"
## xgb.attributes:
## niter
## callbacks:
## cb.evaluation.log()
## # of features: 4
## niter: 1843
## nfeatures : 4
## evaluation_log:
## iter training_rmse
## 1 930166.688
## 2 926389.438
## ---
## 1842 4165.717
## 1843 4156.282
##
## 33.284 sec elapsedensemble_tbl<- modeltime_table(
ensemble_fit_mean,
ensemble_fit_lm,
ensemble_fit_xg
)Ensemble test Accuracy
ensemble_tbl%>%
combine_modeltime_tables(submodels_tbl)%>%
modeltime_accuracy(training(splits))%>%
arrange(rmse,mae,mape,mase)%>%
knitr::kable()| .model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
|---|---|---|---|---|---|---|---|---|
| 4 | PROPHET W/ XGBOOST ERRORS | Fitted | 8358.066 | 4.664870 | 0.2878886 | 5.034977 | 12284.70 | 0.9982249 |
| 5 | XGBOOST | Test | 9057.424 | 2.410889 | 0.3119775 | 2.426319 | 13862.23 | 0.9979730 |
| 1 | ENSEMBLE (MEAN): 4 MODELS | Test | 20188.732 | 11.921483 | 0.6953888 | 10.394124 | 28416.18 | 0.9931428 |
| 6 | RANDOMFOREST | Test | 22228.350 | 27.626853 | 0.7656422 | 17.175190 | 28609.14 | 0.9937744 |
| 7 | KERNLAB | Test | 51133.042 | 27.917171 | 1.7612471 | 25.196393 | 74180.79 | 0.9447666 |
| 2 | ENSEMBLE (GLMNET STACK): 4 MODELS | Test | 88266.294 | 101.030467 | 3.0402798 | 42.730754 | 91977.67 | 0.9910316 |
| 3 | ENSEMBLE (XGBOOST STACK): 4 MODELS | Test | 355159.469 | 746.670336 | 12.2332558 | 78.771919 | 426695.16 | 0.4688337 |
ensemble_tbl%>%
combine_modeltime_tables(submodels_tbl)%>%
modeltime_accuracy(testing(splits))%>%
arrange(rmse,mae,mape,mase)%>%
knitr::kable()| .model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
|---|---|---|---|---|---|---|---|---|
| 2 | ENSEMBLE (GLMNET STACK): 4 MODELS | Test | 39532.79 | 3.868887 | 0.6905913 | 3.853704 | 46523.53 | 0.6450400 |
| 4 | PROPHET W/ XGBOOST ERRORS | Test | 52131.10 | 5.268918 | 0.9106689 | 5.210271 | 64435.97 | 0.9630310 |
| 5 | XGBOOST | Test | 46463.83 | 4.473893 | 0.8116684 | 4.667059 | 66341.33 | 0.5212406 |
| 6 | RANDOMFOREST | Test | 60557.75 | 5.825055 | 1.0578725 | 6.073960 | 74428.34 | 0.9052646 |
| 1 | ENSEMBLE (MEAN): 4 MODELS | Test | 71950.03 | 6.937332 | 1.2568823 | 7.282724 | 87066.96 | 0.8421843 |
| 3 | ENSEMBLE (XGBOOST STACK): 4 MODELS | Test | 88613.23 | 8.942972 | 1.5479688 | 8.810577 | 108793.61 | 0.9913477 |
| 7 | KERNLAB | Test | 199407.40 | 19.831176 | 3.4834126 | 22.083500 | 205148.12 | 0.9719017 |
Ensemble Test Forecast
ensemble_tbl%>%
modeltime_calibrate(testing(splits))%>%
modeltime_forecast(
new_data = testing(splits),
actual_data = data_prepared_tbl,
keep_data = T,
conf_by_id = T,
conf_interval = 0.95
)%>%
plot_modeltime_forecast(
.facet_ncol=2
)ensemble_tbl%>%
combine_modeltime_tables(submodels_tbl)%>%
modeltime_calibrate(testing(splits))%>%
modeltime_forecast(
new_data = testing(splits),
actual_data = data_prepared_tbl,
keep_data = T
)%>%
plot_modeltime_forecast(
.facet_ncol=2
)ensemble_tbl_all_model<-ensemble_tbl%>%
combine_modeltime_tables(submodels_tbl)Refit Ensemble
ensemble_refit_tbl <- ensemble_tbl%>%
modeltime_refit(data_prepared_tbl)Visualize Ensemble Forecast
ensemble_refit_tbl%>%
modeltime_forecast(
new_data = future_tbl,
actual_data = data_prepared_tbl,
keep_data = T
)%>%
plot_modeltime_forecast(
.facet_ncol=2
)Serie en Dolares
DATA
dolares%>%
plot_time_series(Date,value,.facet_ncol = 3, .interactive = F)
DATA PREPARATION
FORECAST_HORIZON <- 5Full = Training + Forecast Dataset
full_data_tbl <- dolares%>%
select(Date,value)%>%
future_frame(
.date_var = Date,
.length_out = FORECAST_HORIZON,
.bind_data = T
)Training Data
data_prepared_tbl <- full_data_tbl[!is.na(full_data_tbl$value),]
# data_prepared_tbl%>%
# tk_summary_diagnostics()Future Data Forecast
future_tbl <- full_data_tbl[is.na(full_data_tbl$value),]SPLITTING
splits <- data_prepared_tbl%>%
arrange(Date)%>%
time_series_split(
data_var=Date,
assess = FORECAST_HORIZON,
cumulative = T
)
splits## <Analysis/Assess/Total>
## <241/5/246>PREPROCESOR
recipe_spec_1 <- recipe(value~., training(splits))%>%
step_timeseries_signature(Date)%>%
## Elimina las columnas o atributos que no aportan
step_rm(matches("(.iso$)|(.xts)|(day)|(hour)|(minute)|(second)|(am.pm)|(week)")) %>%
step_normalize(Date_index.num,Date_year)%>%
step_mutate(Date_month = factor(Date_month,ordered = T))%>%
step_dummy(all_nominal(),one_hot = T)
recipe_spec_1 %>% prep() %>% juice() %>% glimpse()## Rows: 241
## Columns: 30
## $ Date <date> 2001-02-01, 2001-03-01, 2001-04-01, 2001-05-01, 200…
## $ value <dbl> 21.8817, 24.1889, 24.6323, 30.7223, 30.6749, 31.9302…
## $ Date_index.num <dbl> -1.720507, -1.707312, -1.692703, -1.678565, -1.66395…
## $ Date_year <dbl> -1.656391, -1.656391, -1.656391, -1.656391, -1.65639…
## $ Date_half <int> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2…
## $ Date_quarter <int> 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3…
## $ Date_month_01 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ Date_month_02 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ Date_month_03 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
## $ Date_month_04 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
## $ Date_month_05 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ Date_month_06 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ Date_month_07 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ Date_month_08 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_09 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_01 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_02 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ Date_month.lbl_03 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
## $ Date_month.lbl_04 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
## $ Date_month.lbl_05 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ Date_month.lbl_06 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ Date_month.lbl_07 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ Date_month.lbl_08 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_09 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…recipe_spec_2 <- recipe_spec_1%>%
update_role(Date,new_role = "ID")
recipe_spec_2 %>% prep() %>% juice() %>% glimpse()## Rows: 241
## Columns: 30
## $ Date <date> 2001-02-01, 2001-03-01, 2001-04-01, 2001-05-01, 200…
## $ value <dbl> 21.8817, 24.1889, 24.6323, 30.7223, 30.6749, 31.9302…
## $ Date_index.num <dbl> -1.720507, -1.707312, -1.692703, -1.678565, -1.66395…
## $ Date_year <dbl> -1.656391, -1.656391, -1.656391, -1.656391, -1.65639…
## $ Date_half <int> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2…
## $ Date_quarter <int> 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3…
## $ Date_month_01 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ Date_month_02 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ Date_month_03 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
## $ Date_month_04 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
## $ Date_month_05 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ Date_month_06 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ Date_month_07 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ Date_month_08 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_09 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month_12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_01 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_02 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
## $ Date_month.lbl_03 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
## $ Date_month.lbl_04 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
## $ Date_month.lbl_05 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
## $ Date_month.lbl_06 <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
## $ Date_month.lbl_07 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ Date_month.lbl_08 <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_09 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Date_month.lbl_12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…recipe_spec_1 %>% prep() %>% summary()## # A tibble: 30 × 4
## variable type role source
## <chr> <chr> <chr> <chr>
## 1 Date date predictor original
## 2 value numeric outcome original
## 3 Date_index.num numeric predictor derived
## 4 Date_year numeric predictor derived
## 5 Date_half numeric predictor derived
## 6 Date_quarter numeric predictor derived
## 7 Date_month_01 numeric predictor derived
## 8 Date_month_02 numeric predictor derived
## 9 Date_month_03 numeric predictor derived
## 10 Date_month_04 numeric predictor derived
## # … with 20 more rowsrecipe_spec_2 %>% prep() %>% summary()## # A tibble: 30 × 4
## variable type role source
## <chr> <chr> <chr> <chr>
## 1 Date date ID original
## 2 value numeric outcome original
## 3 Date_index.num numeric predictor derived
## 4 Date_year numeric predictor derived
## 5 Date_half numeric predictor derived
## 6 Date_quarter numeric predictor derived
## 7 Date_month_01 numeric predictor derived
## 8 Date_month_02 numeric predictor derived
## 9 Date_month_03 numeric predictor derived
## 10 Date_month_04 numeric predictor derived
## # … with 20 more rowsMODELS
prophet
wflw_fit_prophet <- workflow()%>%
add_model(
prophet_reg() %>% set_engine("prophet")
)%>%
add_recipe(recipe_spec_1)%>%
fit(training(splits))XGBOOST
wflw_fit_xgboost_0_015 <- workflow()%>%
add_model(
boost_tree(learn_rate=0.15) %>% set_engine("xgboost")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))
wflw_fit_xgboost_0_1 <- workflow()%>%
add_model(
boost_tree(learn_rate=0.1) %>% set_engine("xgboost")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))
wflw_fit_xgboost_0_3 <- workflow()%>%
add_model(
boost_tree(learn_rate=0.3) %>% set_engine("xgboost")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))Random Forest
wflw_fit_rf_1000 <- workflow()%>%
add_model(
rand_forest(
trees = 1000
) %>%
set_engine("randomForest")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))
wflw_fit_rf_500 <- workflow()%>%
add_model(
rand_forest(
trees = 500
) %>%
set_engine("randomForest")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))
wflw_fit_rf_200 <- workflow()%>%
add_model(
rand_forest(
trees = 200
) %>%
set_engine("randomForest")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))SVM
wflw_fit_svm <- workflow()%>%
add_model(
svm_rbf() %>% set_engine("kernlab")
)%>%
add_recipe(recipe_spec_2)%>%
fit(training(splits))prophet_boost
wflw_fit_prophet_boost <- workflow()%>%
add_model(
prophet_boost(
seasonality_yearly = F,
seasonality_weekly = F,
seasonality_daily = F,
) %>%
set_engine("prophet_xgboost")
)%>%
add_recipe(recipe_spec_1)%>%
fit(training(splits))MODELTIME WORKFLOW
modeltime table
submodels_tbl <- modeltime_table(
wflw_fit_prophet,
wflw_fit_prophet_boost, #2
#wflw_fit_xgboost_0_015, #3
#wflw_fit_xgboost_0_1, #4
wflw_fit_xgboost_0_3, #5
#wflw_fit_rf_1000, #6
wflw_fit_rf_500 , #7
#wflw_fit_rf_200, #8
wflw_fit_svm #9
)
submodels_tbl## # Modeltime Table
## # A tibble: 5 × 3
## .model_id .model .model_desc
## <int> <list> <chr>
## 1 1 <workflow> PROPHET W/ REGRESSORS
## 2 2 <workflow> PROPHET W/ XGBOOST ERRORS
## 3 3 <workflow> XGBOOST
## 4 4 <workflow> RANDOMFOREST
## 5 5 <workflow> KERNLABcalibrate Testing Data
submodels_calibrated_tbl_train <- submodels_tbl %>%
modeltime_calibrate(training(splits))
submodels_calibrated_tbl_train## # Modeltime Table
## # A tibble: 5 × 5
## .model_id .model .model_desc .type .calibration_data
## <int> <list> <chr> <chr> <list>
## 1 1 <workflow> PROPHET W/ REGRESSORS Fitted <tibble [241 × 4]>
## 2 2 <workflow> PROPHET W/ XGBOOST ERRORS Fitted <tibble [241 × 4]>
## 3 3 <workflow> XGBOOST Test <tibble [241 × 4]>
## 4 4 <workflow> RANDOMFOREST Test <tibble [241 × 4]>
## 5 5 <workflow> KERNLAB Test <tibble [241 × 4]>submodels_calibrated_tbl <- submodels_tbl %>%
modeltime_calibrate(testing(splits))
submodels_calibrated_tbl## # Modeltime Table
## # A tibble: 5 × 5
## .model_id .model .model_desc .type .calibration_data
## <int> <list> <chr> <chr> <list>
## 1 1 <workflow> PROPHET W/ REGRESSORS Test <tibble [5 × 4]>
## 2 2 <workflow> PROPHET W/ XGBOOST ERRORS Test <tibble [5 × 4]>
## 3 3 <workflow> XGBOOST Test <tibble [5 × 4]>
## 4 4 <workflow> RANDOMFOREST Test <tibble [5 × 4]>
## 5 5 <workflow> KERNLAB Test <tibble [5 × 4]>Measure Test Accuracy
submodels_calibrated_tbl_train%>%
modeltime_accuracy()%>%
arrange(rmse)%>%
knitr::kable()| .model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
|---|---|---|---|---|---|---|---|---|
| 2 | PROPHET W/ XGBOOST ERRORS | Fitted | 14.72961 | 5.048915 | 0.3616871 | 4.883939 | 20.37199 | 0.9977092 |
| 3 | XGBOOST | Test | 13.15145 | 2.141262 | 0.3229353 | 2.158493 | 20.71904 | 0.9979108 |
| 4 | RANDOMFOREST | Test | 36.96544 | 19.784290 | 0.9076900 | 13.176725 | 47.40637 | 0.9912356 |
| 1 | PROPHET W/ REGRESSORS | Fitted | 84.72176 | 24.775372 | 2.0803512 | 23.088997 | 109.54362 | 0.9326231 |
| 5 | KERNLAB | Test | 85.53778 | 23.042314 | 2.1003887 | 18.335571 | 114.97273 | 0.9303008 |
submodels_calibrated_tbl%>%
modeltime_accuracy()%>%
arrange(rmse)%>%
knitr::kable()| .model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
|---|---|---|---|---|---|---|---|---|
| 2 | PROPHET W/ XGBOOST ERRORS | Test | 118.0343 | 6.525348 | 1.371201 | 6.833269 | 141.1785 | 0.0918866 |
| 3 | XGBOOST | Test | 168.0157 | 9.335913 | 1.951834 | 9.930132 | 192.0582 | NA |
| 4 | RANDOMFOREST | Test | 310.7488 | 17.555510 | 3.609960 | 19.331450 | 320.6782 | 0.7634197 |
| 1 | PROPHET W/ REGRESSORS | Test | 353.9586 | 20.075326 | 4.111928 | 22.363135 | 359.7840 | 0.9663952 |
| 5 | KERNLAB | Test | 436.0666 | 24.813671 | 5.065774 | 28.356871 | 439.5200 | 0.8531682 |
Visualize test forecast
Predict_ML_USD_train<- submodels_calibrated_tbl %>%
modeltime_forecast(
new_data = training(splits),
actual_data = training(splits),
keep_data = T
)
Predict_ML_USD_train%>%
plot_modeltime_forecast(
.facet_ncol=2
)Predict_ML_USD_test<- submodels_calibrated_tbl %>%
modeltime_forecast(
new_data = testing(splits),
actual_data = testing(splits),
keep_data = T
)
Predict_ML_USD_test%>%
plot_modeltime_forecast(
.facet_ncol=2
)Refit on full training dataset
submodels_refit_tbl <- submodels_calibrated_tbl %>%
modeltime_refit(data_prepared_tbl)Visualize Submodel Forecast
Predict_ML_USD_full <- submodels_refit_tbl%>%
modeltime_forecast(
new_data = future_tbl,
actual_data = data_prepared_tbl,
keep_data = T
)
DT::datatable(Predict_ML_USD_full)Predict_ML_USD_full%>%
plot_modeltime_forecast(
.facet_ncol=2
)Validación cruzada
https://cran.r-project.org/web/packages/modeltime.resample/vignettes/getting-started.html
resamples_tscv <- time_series_cv(
data = data_prepared_tbl,
date_var = Date,
assess = FORECAST_HORIZON,
initial = "36 month",
skip = FORECAST_HORIZON,
slice_limit = 5
)
resamples_tscv## # Time Series Cross Validation Plan
## # A tibble: 5 × 2
## splits id
## <list> <chr>
## 1 <split [36/5]> Slice1
## 2 <split [36/5]> Slice2
## 3 <split [36/5]> Slice3
## 4 <split [36/5]> Slice4
## 5 <split [36/5]> Slice5resamples_tscv %>%
tk_time_series_cv_plan() %>%
plot_time_series_cv_plan(Date,
value,
.facet_ncol = 2,
.interactive = T)Generate Resample Predictions
resamples_fitted <- submodels_tbl %>%
modeltime_fit_resamples(
resamples = resamples_tscv,
control = control_resamples(verbose = FALSE)
)
resamples_fitted## # Modeltime Table
## # A tibble: 5 × 4
## .model_id .model .model_desc .resample_results
## <int> <list> <chr> <list>
## 1 1 <workflow> PROPHET W/ REGRESSORS <rsmp[+]>
## 2 2 <workflow> PROPHET W/ XGBOOST ERRORS <rsmp[+]>
## 3 3 <workflow> XGBOOST <rsmp[+]>
## 4 4 <workflow> RANDOMFOREST <rsmp[+]>
## 5 5 <workflow> KERNLAB <rsmp[+]>Evaluate the Results
resamples_fitted %>%
plot_modeltime_resamples(
.point_size = 3,
.point_alpha = 0.8,
.interactive = T
)resamples_fitted %>%
modeltime_resample_accuracy(summary_fns = mean) %>%
table_modeltime_accuracy(.interactive = T)ENSEMBLE
Ensamble Media y Meta-Learner
ensemble_fit_mean <- submodels_tbl %>%
#filter(!.model_id %in% c(1))%>%
ensemble_average(type="mean")
ensemble_fit_lm <- resamples_fitted %>%
ensemble_model_spec(
model_spec = linear_reg(
penalty = tune(),
mixture = tune()
) %>%
set_engine("glmnet"),
grid = 2,
control = control_grid(verbose = TRUE)
)## ── Tuning Model Specification ───────────────────────────────────
##
##
## # A tibble: 1 × 8
## penalty mixture .metric .estimator mean n std_err .config
## <dbl> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 0.00320 0.973 rmse standard 89.4 5 10.3 Preprocessor1_Model2
##
## # A tibble: 6 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 233. PROPHET W/ REGRESSORS
## 2 2 159. PROPHET W/ XGBOOST ERRORS
## 3 3 189. XGBOOST
## 4 4 207. RANDOMFOREST
## 5 5 295. KERNLAB
## 6 ensemble 66.2 ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
##
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: linear_reg()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 0 Recipe Steps
##
## ── Model ───────────────────────────────────────────────────────────────────────
##
## Call: glmnet::glmnet(x = maybe_matrix(x), y = y, family = "gaussian", alpha = ~0.973376602126518)
##
## Df %Dev Lambda
## 1 0 0.00 221.400
## 2 2 14.61 201.700
## 3 2 27.14 183.800
## 4 3 37.57 167.500
## 5 3 46.27 152.600
## 6 3 53.52 139.100
## 7 3 59.55 126.700
## 8 3 64.56 115.400
## 9 3 68.73 105.200
## 10 3 72.20 95.850
## 11 3 75.08 87.330
## 12 3 77.47 79.570
## 13 3 79.46 72.500
## 14 3 81.11 66.060
## 15 3 82.49 60.190
## 16 3 83.63 54.850
## 17 3 84.58 49.970
## 18 3 85.36 45.530
## 19 3 86.02 41.490
## 20 3 86.56 37.800
## 21 3 87.01 34.450
## 22 3 87.39 31.390
## 23 3 87.70 28.600
## 24 3 87.96 26.060
## 25 3 88.17 23.740
## 26 3 88.35 21.630
## 27 3 88.50 19.710
## 28 3 88.62 17.960
## 29 3 88.73 16.360
## 30 3 88.81 14.910
## 31 3 88.88 13.590
## 32 3 88.94 12.380
## 33 3 88.99 11.280
## 34 3 89.03 10.280
## 35 3 89.06 9.364
## 36 4 89.13 8.532
## 37 4 89.55 7.774
## 38 4 89.88 7.084
## 39 4 90.16 6.454
## 40 4 90.39 5.881
## 41 4 90.58 5.359
## 42 4 90.75 4.883
## 43 4 90.88 4.449
## 44 4 90.99 4.054
## 45 5 91.09 3.693
## 46 5 91.24 3.365
##
## ...
## and 30 more lines.
##
## 7.655 sec elapsedensemble_fit_xg<- resamples_fitted %>%
ensemble_model_spec(
model_spec = boost_tree(
mtry=tune(),
trees=tune(),
learn_rate = tune()
) %>% set_engine("xgboost"),
control = control_grid(verbose = TRUE)
)## ── Tuning Model Specification ───────────────────────────────────
##
##
## # A tibble: 1 × 9
## mtry trees learn_rate .metric .estimator mean n std_err .config
## <int> <int> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 3 1203 0.0112 rmse standard 86.7 5 4.64 Preprocessor1_M…
##
## # A tibble: 6 × 3
## .model_id rmse .model_desc
## <chr> <dbl> <chr>
## 1 1 233. PROPHET W/ REGRESSORS
## 2 2 159. PROPHET W/ XGBOOST ERRORS
## 3 3 189. XGBOOST
## 4 4 207. RANDOMFOREST
## 5 5 295. KERNLAB
## 6 ensemble 0.368 ENSEMBLE (MODEL SPEC)
##
## ── Final Model ──────────────────────────────────────────────────
##
## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: boost_tree()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 0 Recipe Steps
##
## ── Model ───────────────────────────────────────────────────────────────────────
## ##### xgb.Booster
## raw: 1.7 Mb
## call:
## xgboost::xgb.train(params = list(eta = 0.0111638755448734, max_depth = 6,
## gamma = 0, colsample_bytree = 1, colsample_bynode = 0.6,
## min_child_weight = 1, subsample = 1, objective = "reg:squarederror"),
## data = x$data, nrounds = 1203L, watchlist = x$watchlist,
## verbose = 0, nthread = 1)
## params (as set within xgb.train):
## eta = "0.0111638755448734", max_depth = "6", gamma = "0", colsample_bytree = "1", colsample_bynode = "0.6", min_child_weight = "1", subsample = "1", objective = "reg:squarederror", nthread = "1", validate_parameters = "TRUE"
## xgb.attributes:
## niter
## callbacks:
## cb.evaluation.log()
## # of features: 5
## niter: 1203
## nfeatures : 5
## evaluation_log:
## iter training_rmse
## 1 1419.511353
## 2 1404.692627
## ---
## 1202 0.369653
## 1203 0.367557
##
## 32.8 sec elapsedensemble_tbl<- modeltime_table(
ensemble_fit_mean,
ensemble_fit_lm,
ensemble_fit_xg
)Ensemble test Accuracy
ensemble_tbl%>%
combine_modeltime_tables(submodels_tbl)%>%
modeltime_accuracy(training(splits))%>%
arrange(rmse,mae,mape,mase)%>%
knitr::kable()| .model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
|---|---|---|---|---|---|---|---|---|
| 5 | PROPHET W/ XGBOOST ERRORS | Fitted | 14.72961 | 5.048915 | 0.3616871 | 4.883939 | 20.37199 | 0.9977092 |
| 6 | XGBOOST | Test | 13.15145 | 2.141262 | 0.3229353 | 2.158493 | 20.71904 | 0.9979108 |
| 7 | RANDOMFOREST | Test | 36.96544 | 19.784290 | 0.9076900 | 13.176725 | 47.40637 | 0.9912356 |
| 1 | ENSEMBLE (MEAN): 5 MODELS | Test | 42.26934 | 10.988917 | 1.0379279 | 9.503742 | 56.85487 | 0.9837088 |
| 4 | PROPHET W/ REGRESSORS | Fitted | 84.72176 | 24.775372 | 2.0803512 | 23.088997 | 109.54362 | 0.9326231 |
| 8 | KERNLAB | Test | 85.53778 | 23.042314 | 2.1003887 | 18.335571 | 114.97273 | 0.9303008 |
| 2 | ENSEMBLE (GLMNET STACK): 5 MODELS | Test | 384.58760 | 339.894415 | 9.4435869 | 94.291081 | 473.96156 | 0.9737273 |
| 3 | ENSEMBLE (XGBOOST STACK): 5 MODELS | Test | 485.24104 | 418.765819 | 11.9151422 | 68.227791 | 583.22799 | 0.5296009 |
ensemble_tbl%>%
combine_modeltime_tables(submodels_tbl)%>%
modeltime_accuracy(testing(splits))%>%
arrange(rmse,mae,mape,mase)%>%
knitr::kable()| .model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
|---|---|---|---|---|---|---|---|---|
| 2 | ENSEMBLE (GLMNET STACK): 5 MODELS | Test | 57.38698 | 3.393666 | 0.666663 | 3.309372 | 69.20204 | 0.8919348 |
| 3 | ENSEMBLE (XGBOOST STACK): 5 MODELS | Test | 103.55834 | 5.736585 | 1.203034 | 5.963642 | 124.12740 | 0.0750787 |
| 5 | PROPHET W/ XGBOOST ERRORS | Test | 118.03429 | 6.525348 | 1.371201 | 6.833269 | 141.17847 | 0.0918866 |
| 6 | XGBOOST | Test | 168.01570 | 9.335913 | 1.951834 | 9.930132 | 192.05816 | NA |
| 1 | ENSEMBLE (MEAN): 5 MODELS | Test | 275.30170 | 15.533800 | 3.198172 | 16.922376 | 285.57731 | 0.9867409 |
| 7 | RANDOMFOREST | Test | 310.74877 | 17.555510 | 3.609960 | 19.331450 | 320.67819 | 0.7634197 |
| 4 | PROPHET W/ REGRESSORS | Test | 353.95864 | 20.075326 | 4.111928 | 22.363135 | 359.78397 | 0.9663952 |
| 8 | KERNLAB | Test | 436.06661 | 24.813671 | 5.065774 | 28.356871 | 439.52002 | 0.8531682 |
Ensemble Test Forecast
ensemble_tbl%>%
modeltime_calibrate(testing(splits))%>%
modeltime_forecast(
new_data = testing(splits),
actual_data = data_prepared_tbl,
keep_data = T,
conf_by_id = T,
conf_interval = 0.95
)%>%
plot_modeltime_forecast(
.facet_ncol=2
)ensemble_tbl%>%
combine_modeltime_tables(submodels_tbl)%>%
modeltime_calibrate(testing(splits))%>%
modeltime_forecast(
new_data = testing(splits),
actual_data = data_prepared_tbl,
keep_data = T
)%>%
plot_modeltime_forecast(
.facet_ncol=2
)ensemble_tbl_all_model<-ensemble_tbl%>%
combine_modeltime_tables(submodels_tbl)Refit Ensemble
ensemble_refit_tbl <- ensemble_tbl%>%
modeltime_refit(data_prepared_tbl)Visualize Ensemble Forecast
ensemble_refit_tbl%>%
modeltime_forecast(
new_data = future_tbl,
actual_data = data_prepared_tbl,
keep_data = T
)%>%
plot_modeltime_forecast(
.facet_ncol=2
)3.2.4. Resumen de Modelo
3.2.5. Pronóstico
3.3. Prueba de Tensión
4. Conclusiones
5.Anexos
# otlier_crc
# plot(otlier_crc)
# plot(otlier_usd)
# otlier_usd#descompo